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Ranking-Based Siamese Visual Tracking | Feng Tang, Qiang Ling | Current Siamese-based trackers mainly formulate the visual tracking into two indepedent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence of proposals for the final prediction, which may yield the misalignment between classification and localization. To resolve these issues, this paper proposes a ranking-based optimization algorithm to explore the relationship among different proposals. To this end, we introduce two ranking losses, including the classification one and the IoU-guided one, as optimization constraints. The classification ranking loss can ensure that positive samples rank higher than hard negative ones, i.e., distractors, so that the trackers can select the foreground samples successfully without being fooled by the distractors. The IoU-guided ranking loss aims to align classification confidence scores with the Intersection over Union(IoU) of the corresponding localization prediction for positive samples, enabling the well-localized prediction to be represented by high classification confidence. Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference. Extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, NFS30, GOT-10k and LaSOT, demonstrate the effectiveness of the proposed ranking-based optimization algorithm. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Ranking-Based_Siamese_Visual_Tracking_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.11761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Ranking-Based_Siamese_Visual_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Ranking-Based_Siamese_Visual_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
Learnable Lookup Table for Neural Network Quantization | Longguang Wang, Xiaoyu Dong, Yingqian Wang, Li Liu, Wei An, Yulan Guo | Neural network quantization aims at reducing bit-widths of weights and activations for memory and computational efficiency. Since a linear quantizer (i.e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e.g., exponential function) with learnable parameters to build the quantizer for joint optimization. However, these complicated quantizers introduce considerable computational overhead during inference since activation quantization should be conducted online. In this paper, we formulate the quantization process as a simple lookup operation and propose to learn lookup tables as quantizers. Specifically, we develop differentiable lookup tables and introduce several training strategies for optimization. Our lookup tables can be trained with the network in an end-to-end manner to fit the distributions in different layers and have very small additional computational cost. Comparison with previous methods show that quantized networks using our lookup tables achieve state-of-the-art performance on image classification, image super-resolution, and point cloud classification tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learnable_Lookup_Table_for_Neural_Network_Quantization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Learnable_Lookup_Table_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learnable_Lookup_Table_for_Neural_Network_Quantization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learnable_Lookup_Table_for_Neural_Network_Quantization_CVPR_2022_paper.html | CVPR 2022 | null |
SEEG: Semantic Energized Co-Speech Gesture Generation | Yuanzhi Liang, Qianyu Feng, Linchao Zhu, Li Hu, Pan Pan, Yi Yang | Talking gesture generation is a practical yet challenging task which aims to synthesize gestures in line with speech. Gestures with meaningful signs can better convey useful information and arouse sympathy in the audience. Current works focus on aligning gestures with the speech rhythms, which are hard to mine the semantics and model semantic gestures explicitly. In this paper, we propose a novel method SEmantic Energized Generation (SEEG), for semantic-aware gesture generation. Our method contains two parts: DEcoupled Mining module (DEM) and Semantic Energizing Module (SEM). DEM decouples the semantic-irrelevant information from inputs and separately mines information for the beat and semantic gestures. SEM conducts semantic learning and produces semantic gestures. Apart from representational similarity, SEM requires the predictions to express the same semantics as the ground truth. Besides, a semantic prompter is designed in SEM to leverage the semantic-aware supervision to predictions. This promotes the networks to learn and generate semantic gestures. Experimental results reported in three metrics on different benchmarks prove that SEEG efficiently mines semantic cues and generates semantic gestures. In comparison, SEEG outperforms other methods in all semantic-aware evaluations on different datasets. Qualitative evaluations also indicate the superiority of SEEG in semantic expressiveness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_SEEG_Semantic_Energized_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_SEEG_Semantic_Energized_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
AdaViT: Adaptive Vision Transformers for Efficient Image Recognition | Lingchen Meng, Hengduo Li, Bor-Chun Chen, Shiyi Lan, Zuxuan Wu, Yu-Gang Jiang, Ser-Nam Lim | Built on top of self-attention mechanisms, vision transformers have demonstrated remarkable performance on a variety of vision tasks recently. While achieving excellent performance, they still require relatively intensive computational cost that scales up drastically as the numbers of patches, self-attention heads and transformer blocks increase. In this paper, we argue that due to the large variations among images, their need for modeling long-range dependencies between patches differ. To this end, we introduce AdaViT, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition. Optimized jointly with a transformer backbone in an end-to-end manner, a light-weight decision network is attached to the backbone to produce decisions on-the-fly. Extensive experiments on ImageNet demonstrate that our method obtains more than 2x improvement on efficiency compared to state-of-the-art vision transformers with only 0.8% drop of accuracy, achieving good efficiency/accuracy trade-offs conditioned on different computational budgets. We further conduct quantitative and qualitative analysis on learned usage polices and provide more insights on the redundancy in vision transformers. | https://openaccess.thecvf.com/content/CVPR2022/papers/Meng_AdaViT_Adaptive_Vision_Transformers_for_Efficient_Image_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Meng_AdaViT_Adaptive_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15668 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Meng_AdaViT_Adaptive_Vision_Transformers_for_Efficient_Image_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Meng_AdaViT_Adaptive_Vision_Transformers_for_Efficient_Image_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Compound Domain Generalization via Meta-Knowledge Encoding | Chaoqi Chen, Jiongcheng Li, Xiaoguang Han, Xiaoqing Liu, Yizhou Yu | Domain generalization (DG) aims to improve the generalization performance for an unseen target domain by using the knowledge of multiple seen source domains. Mainstream DG methods typically assume that the domain label of each source sample is known a priori, which is challenged to be satisfied in many real-world applications. In this paper, we study a practical problem of compound DG, which relaxes the discrete domain assumption to the mixed source domains setting. On the other hand, current DG algorithms prioritize the focus on semantic invariance across domains (one-vs-one), while paying less attention to the holistic semantic structure (many-vs-many). Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations. To this end, we present Compound Domain Generalization via Meta-Knowledge Encoding (COMEN), a general approach to automatically discover and model latent domains in two steps. Firstly, we introduce Style-induced Domain-specific Normalization (SDNorm) to re-normalize the multi-modal underlying distributions, thereby dividing the mixture of source domains into latent clusters. Secondly, we harness the prototype representations, the centroids of classes, to perform relational modeling in the embedding space with two parallel and complementary modules, which explicitly encode the semantic structure for the out-of-distribution generalization. Experiments on four standard DG benchmarks reveal that COMEN exceeds the state-of-the-art performance without the need of domain supervision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Compound_Domain_Generalization_via_Meta-Knowledge_Encoding_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13006 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Compound_Domain_Generalization_via_Meta-Knowledge_Encoding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Compound_Domain_Generalization_via_Meta-Knowledge_Encoding_CVPR_2022_paper.html | CVPR 2022 | null |
NAN: Noise-Aware NeRFs for Burst-Denoising | Naama Pearl, Tali Treibitz, Simon Korman | Burst denoising is now more relevant than ever, as computational photography helps overcome sensitivity issues inherent in mobile phones and small cameras. A major challenge in burst-denoising is in coping with pixel misalignment, which was so far handled with rather simplistic assumptions of simple motion, or the ability to align in pre-processing. Such assumptions are not realistic in the presence of large motion and high levels of noise. We show that Neural Radiance Fields (NeRFs), originally suggested for physics-based novel-view rendering, can serve as a powerful framework for burst denoising. NeRFs have an inherent capability of handling noise as they integrate information from multiple images, but they are limited in doing so, mainly since they build on pixel-wise operations which are suitable to ideal imaging conditions. Our approach, termed NAN, leverages inter-view and spatial information in NeRFs to better deal with noise. It achieves state-of-the-art results in burst denoising and is especially successful in coping with large movement and occlusions, under very high levels of noise. With the rapid advances in accelerating NeRFs, it could provide a powerful platform for denoising in challenging environments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pearl_NAN_Noise-Aware_NeRFs_for_Burst-Denoising_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.04668 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pearl_NAN_Noise-Aware_NeRFs_for_Burst-Denoising_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pearl_NAN_Noise-Aware_NeRFs_for_Burst-Denoising_CVPR_2022_paper.html | CVPR 2022 | null |
Physical Inertial Poser (PIP): Physics-Aware Real-Time Human Motion Tracking From Sparse Inertial Sensors | Xinyu Yi, Yuxiao Zhou, Marc Habermann, Soshi Shimada, Vladislav Golyanik, Christian Theobalt, Feng Xu | Motion capture from sparse inertial sensors has shown great potential compared to image-based approaches since occlusions do not lead to a reduced tracking quality and the recording space is not restricted to be within the viewing frustum of the camera. However, capturing the motion and global position only from a sparse set of inertial sensors is inherently ambiguous and challenging. In consequence, recent state-of-the-art methods can barely handle very long period motions, and unrealistic artifacts are common due to the unawareness of physical constraints. To this end, we present the first method which combines a neural kinematics estimator and a physics-aware motion optimizer to track body motions with only 6 inertial sensors. The kinematics module first regresses the motion status as a reference, and then the physics module refines the motion to satisfy the physical constraints. Experiments demonstrate a clear improvement over the state of the art in terms of capture accuracy, temporal stability, and physical correctness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yi_Physical_Inertial_Poser_PIP_Physics-Aware_Real-Time_Human_Motion_Tracking_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yi_Physical_Inertial_Poser_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08528 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yi_Physical_Inertial_Poser_PIP_Physics-Aware_Real-Time_Human_Motion_Tracking_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yi_Physical_Inertial_Poser_PIP_Physics-Aware_Real-Time_Human_Motion_Tracking_From_CVPR_2022_paper.html | CVPR 2022 | null |
b-DARTS: Beta-Decay Regularization for Differentiable Architecture Search | Peng Ye, Baopu Li, Yikang Li, Tao Chen, Jiayuan Fan, Wanli Ouyang | Neural Architecture Search (NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural network automatically. Among them, differential NAS approaches such as DARTS, have gained popularity for the search efficiency. However, they suffer from two main issues, the weak robustness to the performance collapse and the poor generalization ability of the searched architectures. To solve these two problems, a simple-but-efficient regularization method, termed as Beta-Decay, is proposed to regularize the DARTS-based NAS searching process. Specifically, Beta-Decay regularization can impose constraints to keep the value and variance of activated architecture parameters from too large. Furthermore, we provide in-depth theoretical analysis on how it works and why it works. Experimental results on NAS-Bench-201 show that our proposed method can help to stabilize the searching process and makes the searched network more transferable across different datasets. In addition, our search scheme shows an outstanding property of being less dependent on training time and data. Comprehensive experiments on a variety of search spaces and datasets validate the effectiveness of the proposed method. The code is available at https://github.com/Sunshine-Ye/Beta-DARTS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_b-DARTS_Beta-Decay_Regularization_for_Differentiable_Architecture_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_b-DARTS_Beta-Decay_Regularization_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_b-DARTS_Beta-Decay_Regularization_for_Differentiable_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_b-DARTS_Beta-Decay_Regularization_for_Differentiable_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
Vector Quantized Diffusion Model for Text-to-Image Synthesis | Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, Baining Guo | We present the vector quantized diffusion (VQ-Diffusion) model for text-to-image generation. This method is based on a vector quantized variational autoencoder (VQ-VAE) whose latent space is modeled by a conditional variant of the recently developed Denoising Diffusion Probabilistic Model (DDPM). We find that this latent-space method is well-suited for text-to-image generation tasks because it not only eliminates the unidirectional bias with existing methods but also allows us to incorporate a mask-and-replace diffusion strategy to avoid the accumulation of errors, which is a serious problem with existing methods. Our experiments show that the VQ-Diffusion produces significantly better text-to-image generation results when compared with conventional autoregressive (AR) models with similar numbers of parameters. Compared with previous GAN-based text-to-image methods, our VQ-Diffusion can handle more complex scenes and improve the synthesized image quality by a large margin. Finally, we show that the image generation computation in our method can be made highly efficient by reparameterization. With traditional AR methods, the text-to-image generation time increases linearly with the output image resolution and hence is quite time consuming even for normal size images. The VQ-Diffusion allows us to achieve a better trade-off between quality and speed. Our experiments indicate that the VQ-Diffusion model with the reparameterization is fifteen times faster than traditional AR methods while achieving a better image quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_Vector_Quantized_Diffusion_Model_for_Text-to-Image_Synthesis_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.14822 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Vector_Quantized_Diffusion_Model_for_Text-to-Image_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_Vector_Quantized_Diffusion_Model_for_Text-to-Image_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
CMT: Convolutional Neural Networks Meet Vision Transformers | Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, Chang Xu | Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image. However, there are still gaps in both performance and computational cost between transformers and existing convolutional neural networks (CNNs). In this paper, we aim to address this issue and develop a network that can outperform not only the canonical transformers, but also the high-performance convolutional models. We propose a new transformer based hybrid network by taking advantage of transformers to capture long-range dependencies, and of CNNs to extract local information. Furthermore, we scale it to obtain a family of models, called CMTs, obtaining much better trade-off for accuracy and efficiency than previous CNN-based and transformer-based models. In particular, our CMT-S achieves 83.5% top-1 accuracy on ImageNet, while being 14x and 2x smaller on FLOPs than the existing DeiT and EfficientNet, respectively. The proposed CMT-S also generalizes well on CIFAR10 (99.2%), CIFAR100 (91.7%), Flowers (98.7%), and other challenging vision datasets such as COCO (44.3% mAP), with considerably less computational cost. Code is available at https://github.com/ggjy/CMT.pytorch. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_CMT_Convolutional_Neural_Networks_Meet_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_CMT_Convolutional_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.06263 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_CMT_Convolutional_Neural_Networks_Meet_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_CMT_Convolutional_Neural_Networks_Meet_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Hyperspherical Consistency Regularization | Cheng Tan, Zhangyang Gao, Lirong Wu, Siyuan Li, Stan Z. Li | Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning with a shared feature encoder becomes a common scheme. Though it benefits from taking advantage of both feature-dependent information from self-supervised learning and label-dependent information from supervised learning, this scheme remains suffering from bias of the classifier. In this work, we systematically explore the relationship between self-supervised learning and supervised learning, and study how self-supervised learning helps robust data-efficient deep learning. We propose hyperspherical consistency regularization (HCR), a simple yet effective plug-and-play method, to regularize the classifier using feature-dependent information and thus avoid bias from labels. Specifically, HCR first project logits from the classifier and feature projections from the projection head on the respective hypersphere, then it enforces data points on hyperspheres to have similar structures by minimizing binary cross entropy of pairwise distances' similarity metrics. Extensive experiments on semi-supervised learning and weakly-supervised learning demonstrate the effectiveness of our proposed method, by showing superior performance with HCR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tan_Hyperspherical_Consistency_Regularization_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2206.00845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tan_Hyperspherical_Consistency_Regularization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tan_Hyperspherical_Consistency_Regularization_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Image-to-Image Translation With Generative Prior | Shuai Yang, Liming Jiang, Ziwei Liu, Chen Change Loy | Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a novel framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g., BigGAN) to learn rich content correspondences across various domains. We propose a novel coarse-to-fine scheme: we first distill the generative prior to capture a robust coarse-level content representation that can link objects at an abstract semantic level, based on which fine-level content features are adaptively learned for more accurate multi-level content correspondences. Extensive experiments demonstrate the superiority of our versatile framework over state-of-the-art methods in robust, high-quality and diversified translations, even for challenging and distant domains. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Unsupervised_Image-to-Image_Translation_With_Generative_Prior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Unsupervised_Image-to-Image_Translation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03641 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unsupervised_Image-to-Image_Translation_With_Generative_Prior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unsupervised_Image-to-Image_Translation_With_Generative_Prior_CVPR_2022_paper.html | CVPR 2022 | null |
KNN Local Attention for Image Restoration | Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, Dongbo Min | Recent works attempt to integrate the non-local operation with CNNs or Transformer, achieving remarkable performance in image restoration tasks. The global similarity, however, has the problems of the lack of locality and the high computational complexity that is quadratic to an input resolution. The local attention mechanism alleviates these issues by introducing the inductive bias of the locality with convolution-like operators. However, by focusing only on adjacent positions, the local attention suffers from an insufficient receptive field for image restoration. In this paper, we propose a new attention mechanism for image restoration, called k-NN Image Transformer (KiT), that rectifies above mentioned limitations. Specifically, the KiT groups k-nearest neighbor patches with locality sensitive hashing (LSH), and the grouped patches are aggregated into each query patch by performing a pair-wise local attention. In this way, the pair-wise operation establishes non-local connectivity while maintaining the desired properties of the local attention, i.e., inductive bias of locality and linear complexity to input resolution. The proposed method outperforms state-of-the-art restoration approaches on image denoising, deblurring and deraining benchmarks. The code will be available at https://sites.google.com/view/cvpr22-kit. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_KNN_Local_Attention_for_Image_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_KNN_Local_Attention_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_KNN_Local_Attention_for_Image_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_KNN_Local_Attention_for_Image_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
Face Relighting With Geometrically Consistent Shadows | Andrew Hou, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu | Most face relighting methods are able to handle diffuse shadows, but struggle to handle hard shadows, such as those cast by the nose. Methods that propose techniques for handling hard shadows often do not produce geometrically consistent shadows since they do not directly leverage the estimated face geometry while synthesizing them. We propose a novel differentiable algorithm for synthesizing hard shadows based on ray tracing, which we incorporate into training our face relighting model. Our proposed algorithm directly utilizes the estimated face geometry to synthesize geometrically consistent hard shadows. We demonstrate through quantitative and qualitative experiments on Multi-PIE and FFHQ that our method produces more geometrically consistent shadows than previous face relighting methods while also achieving state-of-the-art face relighting performance under directional lighting. In addition, we demonstrate that our differentiable hard shadow modeling improves the quality of the estimated face geometry over diffuse shading models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hou_Face_Relighting_With_Geometrically_Consistent_Shadows_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hou_Face_Relighting_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16681 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hou_Face_Relighting_With_Geometrically_Consistent_Shadows_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hou_Face_Relighting_With_Geometrically_Consistent_Shadows_CVPR_2022_paper.html | CVPR 2022 | null |
Open-Set Text Recognition via Character-Context Decoupling | Chang Liu, Chun Yang, Xu-Cheng Yin | The open-set text recognition task is an emerging challenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limited performance for current methods is the confounding effect of contextual information over the visual information of individual characters. Under open-set scenarios, the intractable bias in contextual information can be passed down to visual information, consequently impairing the classification performance. In this paper, a Character-Context Decoupling framework is proposed to alleviate this problem by separating contextual information and character-visual information. Contextual information can be decomposed into temporal information and linguistic information. Here, temporal information that models character order and word length is isolated with a detached temporal attention module. Linguistic information that models n-gram and other linguistic statistics is separated with a decoupled context anchor mechanism. A variety of quantitative and qualitative experiments show that our method achieves promising performance on open-set, zero-shot, and close-set text recognition datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Open-Set_Text_Recognition_via_Character-Context_Decoupling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Open-Set_Text_Recognition_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05535 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Open-Set_Text_Recognition_via_Character-Context_Decoupling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Open-Set_Text_Recognition_via_Character-Context_Decoupling_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Marginal Contrastive Learning for Multi-Label Subcellular Protein Localization | Ziyi Liu, Zengmao Wang, Bo Du | Protein subcellular localization(PSL) is an important task to study human cell functions and cancer pathogenesis. It has attracted great attention in the computer vision community. However, the huge size of immune histochemical (IHC) images, the disorganized location distribution in different tissue images and the limited training images are always the challenges for the PSL to learn a strong generalization model with deep learning. In this paper, we propose a deep protein subcellular localization method with multi-marginal contrastive learning to perceive the same PSLs in different tissue images and different PSLs within the same tissue image. In the proposed method, we learn the representation of an IHC image by fusing the global features from the downsampled images and local features from the selected patches with the activation map to tackle the oversize of an IHC image. Then a multi-marginal attention mechanism is proposed to generate contrastive pairs with different margins and improve the discriminative features of PSL patterns effectively. Finally, the ensemble prediction of each IHC image is obtained with different patches. The results on the benchmark datasets show that the proposed method achieves the significant improvements for the PSL task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Multi-Marginal_Contrastive_Learning_for_Multi-Label_Subcellular_Protein_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Multi-Marginal_Contrastive_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Multi-Marginal_Contrastive_Learning_for_Multi-Label_Subcellular_Protein_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Multi-Marginal_Contrastive_Learning_for_Multi-Label_Subcellular_Protein_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences | Prune Truong, Martin Danelljan, Fisher Yu, Luc Van Gool | We propose Probabilistic Warp Consistency, a weakly-supervised learning objective for semantic matching. Our approach directly supervises the dense matching scores predicted by the network, encoded as a conditional probability distribution. We first construct an image triplet by applying a known warp to one of the images in a pair depicting different instances of the same object class. Our probabilistic learning objectives are then derived using the constraints arising from the resulting image triplet. We further account for occlusion and background clutter present in real image pairs by extending our probabilistic output space with a learnable unmatched state. To supervise it, we design an objective between image pairs depicting different object classes. We validate our method by applying it to four recent semantic matching architectures. Our weakly-supervised approach sets a new state-of-the-art on four challenging semantic matching benchmarks. Lastly, we demonstrate that our objective also brings substantial improvements in the strongly-supervised regime, when combined with keypoint annotations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Truong_Probabilistic_Warp_Consistency_for_Weakly-Supervised_Semantic_Correspondences_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Truong_Probabilistic_Warp_Consistency_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04279 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Truong_Probabilistic_Warp_Consistency_for_Weakly-Supervised_Semantic_Correspondences_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Truong_Probabilistic_Warp_Consistency_for_Weakly-Supervised_Semantic_Correspondences_CVPR_2022_paper.html | CVPR 2022 | null |
Predict, Prevent, and Evaluate: Disentangled Text-Driven Image Manipulation Empowered by Pre-Trained Vision-Language Model | Zipeng Xu, Tianwei Lin, Hao Tang, Fu Li, Dongliang He, Nicu Sebe, Radu Timofte, Luc Van Gool, Errui Ding | To achieve disentangled image manipulation, previous works depend heavily on manual annotation. Meanwhile, the available manipulations are limited to a pre-defined set the models were trained for. We propose a novel framework, i.e., Predict, Prevent, and Evaluate (PPE), for disentangled text-driven image manipulation that requires little manual annotation while being applicable to a wide variety of manipulations. Our method approaches the targets by deeply exploiting the power of the large-scale pre-trained vision-language model CLIP. Concretely, we firstly Predict the possibly entangled attributes for a given text command. Then, based on the predicted attributes, we introduce an entanglement loss to Prevent entanglements during training. Finally, we propose a new evaluation metric to Evaluate the disentangled image manipulation. We verify the effectiveness of our method on the challenging face editing task. Extensive experiments show that the proposed PPE framework achieves much better quantitative and qualitative results than the up-to-date StyleCLIP baseline. Code is available at https://github.com/zipengxuc/PPE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Predict_Prevent_and_Evaluate_Disentangled_Text-Driven_Image_Manipulation_Empowered_by_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Predict_Prevent_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Predict_Prevent_and_Evaluate_Disentangled_Text-Driven_Image_Manipulation_Empowered_by_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Predict_Prevent_and_Evaluate_Disentangled_Text-Driven_Image_Manipulation_Empowered_by_CVPR_2022_paper.html | CVPR 2022 | null |
Optimizing Elimination Templates by Greedy Parameter Search | Evgeniy Martyushev, Jana Vráblíková, Tomas Pajdla | We propose a new method for constructing elimination templates for efficient polynomial system solving of minimal problems in structure from motion, image matching, and camera tracking. We first construct a particular affine parameterization of the elimination templates for systems with a finite number of distinct solutions. Then, we use a heuristic greedy optimization strategy over the space of parameters to get a template with a small size. We test our method on 34 minimal problems in computer vision. For all of them, we found the templates either of the same or smaller size compared to the state-of-the-art. For some difficult examples, our templates are, e.g., 2.1, 2.5, 3.8, 6.6 times smaller. For the problem of refractive absolute pose estimation with unknown focal length, we have found a template that is 20 times smaller. Our experiments on synthetic data also show that the new solvers are fast and numerically accurate. We also present a fast and numerically accurate solver for the problem of relative pose estimation with unknown common focal length and radial distortion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Martyushev_Optimizing_Elimination_Templates_by_Greedy_Parameter_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Martyushev_Optimizing_Elimination_Templates_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Martyushev_Optimizing_Elimination_Templates_by_Greedy_Parameter_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Martyushev_Optimizing_Elimination_Templates_by_Greedy_Parameter_Search_CVPR_2022_paper.html | CVPR 2022 | null |
TransMix: Attend To Mix for Vision Transformers | Jie-Neng Chen, Shuyang Sun, Ju He, Philip H.S. Torr, Alan Yuille, Song Bai | Mixup-based augmentation has been found to be effective for generalizing models during training, especially for Vision Transformers (ViTs) since they can easily overfit. However, previous mixup-based methods have an underlying prior knowledge that the linearly interpolated ratio of targets should be kept the same as the ratio proposed in input interpolation. This may lead to a strange phenomenon that sometimes there is no valid object in the mixed image due to the random process in augmentation but there is still response in the label space. To bridge such gap between the input and label spaces, we propose TransMix, which mixes labels based on the attention maps of Vision Transformers. The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map. TransMix is embarrassingly simple and can be implemented in just a few lines of code without introducing any extra parameters and FLOPs to ViT-based models. Experimental results show that our method can consistently improve various ViT-based models at scales on ImageNet classification. After pre-trained with TransMix on ImageNet, the ViT-based models also demonstrate better transferability to semantic segmentation, object detection and instance segmentation. TransMix also exhibits to be more robust when evaluating on 4 different benchmarks. Code is publicly available at https://github.com/Beckschen/TransMix. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_TransMix_Attend_To_Mix_for_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_TransMix_Attend_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09833 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_TransMix_Attend_To_Mix_for_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_TransMix_Attend_To_Mix_for_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
HOP: History-and-Order Aware Pre-Training for Vision-and-Language Navigation | Yanyuan Qiao, Yuankai Qi, Yicong Hong, Zheng Yu, Peng Wang, Qi Wu | Pre-training has been adopted in a few of recent works for Vision-and-Language Navigation (VLN). However, previous pre-training methods for VLN either lack the ability to predict future actions or ignore the trajectory contexts, which are essential for a greedy navigation process. In this work, to promote the learning of spatio-temporal visual-textual correspondence as well as the agent's capability of decision making, we propose a novel history-and-order aware pre-training paradigm (HOP) with VLN-specific objectives that exploit the past observations and support future action prediction. Specifically, in addition to the commonly used Masked Language Modeling (MLM) and Trajectory-Instruction Matching (TIM), we design two proxy tasks to model temporal order information: Trajectory Order Modeling (TOM) and Group Order Modeling (GOM). Moreover, our navigation action prediction is also enhanced by introducing the task of Action Prediction with History (APH), which takes into account the history visual perceptions. Extensive experimental results on four downstream VLN tasks (R2R, REVERIE, NDH, RxR) demonstrate the effectiveness of our proposed method compared against several state-of-the-art agents. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qiao_HOP_History-and-Order_Aware_Pre-Training_for_Vision-and-Language_Navigation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qiao_HOP_History-and-Order_Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qiao_HOP_History-and-Order_Aware_Pre-Training_for_Vision-and-Language_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qiao_HOP_History-and-Order_Aware_Pre-Training_for_Vision-and-Language_Navigation_CVPR_2022_paper.html | CVPR 2022 | null |
Inertia-Guided Flow Completion and Style Fusion for Video Inpainting | Kaidong Zhang, Jingjing Fu, Dong Liu | Physical objects have inertia, which resists changes in the velocity and motion direction. Inspired by this, we introduce inertia prior that optical flow, which reflects object motion in a local temporal window, keeps unchanged in the adjacent preceding or subsequent frame. We propose a flow completion network to align and aggregate flow features from the consecutive flow sequences based on the inertia prior. The corrupted flows are completed under the supervision of customized losses on reconstruction, flow smoothness, and consistent ternary census transform. The completed flows with high fidelity give rise to significant improvement on the video inpainting quality. Nevertheless, the existing flow-guided cross-frame warping methods fail to consider the lightening and sharpness variation across video frames, which leads to spatial incoherence after warping from other frames. To alleviate such problem, we propose the Adaptive Style Fusion Network (ASFN), which utilizes the style information extracted from the valid regions to guide the gradient refinement in the warped regions. Moreover, we design a data simulation pipeline to reduce the training difficulty of ASFN. Extensive experiments show the superiority of our method against the state-of-the-art methods quantitatively and qualitatively. The project page is at https://github.com/hitachinsk/ISVI | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Inertia-Guided_Flow_Completion_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Inertia-Guided_Flow_Completion_and_Style_Fusion_for_Video_Inpainting_CVPR_2022_paper.html | CVPR 2022 | null |
RU-Net: Regularized Unrolling Network for Scene Graph Generation | Xin Lin, Changxing Ding, Jing Zhang, Yibing Zhan, Dacheng Tao | Scene graph generation (SGG) aims to detect objects and predict the relationships between each pair of objects. Existing SGG methods usually suffer from several issues, including 1) ambiguous object representations, as graph neural network-based message passing (GMP) modules are typically sensitive to spurious inter-node correlations, and 2) low diversity in relationship predictions due to severe class imbalance and a large number of missing annotations. To address both problems, in this paper, we propose a regularized unrolling network (RU-Net). We first study the relation between GMP and graph Laplacian denoising (GLD) from the perspective of the unrolling technique, determining that GMP can be formulated as a solver for GLD. Based on this observation, we propose an unrolled message passing module and introduce an l_p-based graph regularization to suppress spurious connections between nodes. Second, we propose a group diversity enhancement module that promotes the prediction diversity of relationships via rank maximization. Systematic experiments demonstrate that RU-Net is effective under a variety of settings and metrics. Furthermore, RU-Net achieves new state-of-the-arts on three popular databases: VG, VRD, and OI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_RU-Net_Regularized_Unrolling_Network_for_Scene_Graph_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_RU-Net_Regularized_Unrolling_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_RU-Net_Regularized_Unrolling_Network_for_Scene_Graph_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_RU-Net_Regularized_Unrolling_Network_for_Scene_Graph_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Long-Tailed Visual Recognition via Gaussian Clouded Logit Adjustment | Mengke Li, Yiu-ming Cheung, Yang Lu | Long-tailed data is still a big challenge for deep neural networks, even though they have achieved great success on balanced data. We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes, which leads to difficulty in classifying tail class samples. Furthermore, the original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. This phenomenon is called softmax saturation. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data, thereby solving the distorted embedding space of long-tailed problems. To this end, this paper proposes the Gaussian clouded logit adjustment by Gaussian perturbation of different class logits with varied amplitude. We define the amplitude of perturbation as cloud size and set relatively large cloud sizes to tail classes. The large cloud size can reduce the softmax saturation and thereby making tail class samples more active as well as enlarging the embedding space. To alleviate the bias in a classifier, we therefore propose the class-based effective number sampling strategy with classifier re-training. Extensive experiments on benchmark datasets validate the superior performance of the proposed method. Source code is available at: https://github.com/Keke921/GCLLoss. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Long-Tailed_Visual_Recognition_via_Gaussian_Clouded_Logit_Adjustment_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Long-Tailed_Visual_Recognition_via_Gaussian_Clouded_Logit_Adjustment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Long-Tailed_Visual_Recognition_via_Gaussian_Clouded_Logit_Adjustment_CVPR_2022_paper.html | CVPR 2022 | null |
Image Animation With Perturbed Masks | Yoav Shalev, Lior Wolf | We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without the knowledge of the object's structure. Furthermore, both, the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object's general pose and shape. To control the source of the identity of the output frame, we employ perturbations to interrupt the unwanted identity information on the driver's mask. A mask-refinement module then replaces the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multi-scale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Due to the lack of fully supervised data, we train on the task of reconstructing frames from the same video the source image is taken from. Our method is shown to greatly outperform the state-of-the-art methods on multiple benchmarks. Our code and samples are available at https://github.com/itsyoavshalev/Image-Animation-with-Perturbed-Masks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shalev_Image_Animation_With_Perturbed_Masks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shalev_Image_Animation_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.06922 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shalev_Image_Animation_With_Perturbed_Masks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shalev_Image_Animation_With_Perturbed_Masks_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring the Equivalence of Siamese Self-Supervised Learning via a Unified Gradient Framework | Chenxin Tao, Honghui Wang, Xizhou Zhu, Jiahua Dong, Shiji Song, Gao Huang, Jifeng Dai | Self-supervised learning has shown its great potential to extract powerful visual representations without human annotations. Various works are proposed to deal with self-supervised learning from different perspectives: (1) contrastive learning methods (e.g., MoCo, SimCLR) utilize both positive and negative samples to guide the training direction; (2) asymmetric network methods (e.g., BYOL, SimSiam) get rid of negative samples via the introduction of a predictor network and the stop-gradient operation; (3) feature decorrelation methods (e.g., Barlow Twins, VICReg) instead aim to reduce the redundancy between feature dimensions. These methods appear to be quite different in the designed loss functions from various motivations. The final accuracy numbers also vary, where different networks and tricks are utilized in different works. In this work, we demonstrate that these methods can be unified into the same form. Instead of comparing their loss functions, we derive a unified formula through gradient analysis. Furthermore, we conduct fair and detailed experiments to compare their performances. It turns out that there is little gap between these methods, and the use of momentum encoder is the key factor to boost performance. From this unified framework, we propose UniGrad, a simple but effective gradient form for self-supervised learning. It does not require a memory bank or a predictor network, but can still achieve state-of-the-art performance and easily adopt other training strategies. Extensive experiments on linear evaluation and many downstream tasks also show its effectiveness. Code shall be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tao_Exploring_the_Equivalence_of_Siamese_Self-Supervised_Learning_via_a_Unified_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tao_Exploring_the_Equivalence_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05141 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Exploring_the_Equivalence_of_Siamese_Self-Supervised_Learning_via_a_Unified_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tao_Exploring_the_Equivalence_of_Siamese_Self-Supervised_Learning_via_a_Unified_CVPR_2022_paper.html | CVPR 2022 | null |
Point Density-Aware Voxels for LiDAR 3D Object Detection | Jordan S. K. Hu, Tianshu Kuai, Steven L. Waslander | LiDAR has become one of the primary 3D object detection sensors in autonomous driving. However, LiDAR's diverging point pattern with increasing distance results in a non-uniform sampled point cloud ill-suited to discretized volumetric feature extraction. Current methods either rely on voxelized point clouds or use inefficient farthest point sampling to mitigate detrimental effects caused by density variation but largely ignore point density as a feature and its predictable relationship with distance from the LiDAR sensor. Our proposed solution, Point Density-Aware Voxel network (PDV), is an end-to-end two stage LiDAR 3D object detection architecture that is designed to account for these point density variations. PDV efficiently localizes voxel features from the 3D sparse convolution backbone through voxel point centroids. The spatially localized voxel features are then aggregated through a density-aware RoI grid pooling module using kernel density estimation (KDE) and self-attention with point density positional encoding. Finally, we exploit LiDAR's point density to distance relationship to refine our final bounding box confidences. PDV outperforms all state-of-the-art methods on the Waymo Open Dataset and achieves competitive results on the KITTI dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Point_Density-Aware_Voxels_for_LiDAR_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Point_Density-Aware_Voxels_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05662 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Point_Density-Aware_Voxels_for_LiDAR_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Point_Density-Aware_Voxels_for_LiDAR_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Integrating Language Guidance Into Vision-Based Deep Metric Learning | Karsten Roth, Oriol Vinyals, Zeynep Akata | Deep Metric Learning (DML) proposes to learn metric spaces which encode semantic similarities as embedding space distances. These spaces should be transferable to classes beyond those seen during training. Commonly, DML methods task networks to solve contrastive ranking tasks defined over binary class assignments. However, such approaches ignore higher-level semantic relations between the actual classes. This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes, impacting the generalizability of the learned metric space. To tackle this issue, we propose a language guidance objective for visual similarity learning. Leveraging language embeddings of expert- and pseudo-classnames, we contextualize and realign visual representation spaces corresponding to meaningful language semantics for better semantic consistency. Extensive experiments and ablations provide a strong motivation for our proposed approach and show language guidance offering significant, model-agnostic improvements for DML, achieving competitive and state-of-the-art results on all benchmarks. Code available at github.com/ExplainableML/LanguageGuidance_for_DML. | https://openaccess.thecvf.com/content/CVPR2022/papers/Roth_Integrating_Language_Guidance_Into_Vision-Based_Deep_Metric_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Roth_Integrating_Language_Guidance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08543 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Integrating_Language_Guidance_Into_Vision-Based_Deep_Metric_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Integrating_Language_Guidance_Into_Vision-Based_Deep_Metric_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
PartGlot: Learning Shape Part Segmentation From Language Reference Games | Juil Koo, Ian Huang, Panos Achlioptas, Leonidas J. Guibas, Minhyuk Sung | We introduce PartGlot, a neural framework and associated architectures for learning semantic part segmentation of 3D shape geometry, based solely on part referential language. We exploit the fact that linguistic descriptions of a shape can provide priors on the shape's parts -- as natural language has evolved to reflect human perception of the compositional structure of objects, essential to their recognition and use. For training we use the paired geometry / language data collected in the ShapeGlot work for their reference game, where a speaker creates an utterance to differentiate a target shape from two distractors and the listener has to find the target based on this utterance. Our network is designed to solve this target discrimination problem, carefully incorporating a Transformer-based attention module so that the output attention can precisely highlight the semantic part or parts described in the language. Furthermore, the network operates without any direct supervision on the 3D geometry itself. Surprisingly, we further demonstrate that the learned part information is generalizable to shape classes unseen during training.Our approach opens the possibility of learning 3D shape parts from language alone, without the need for large-scale part geometry annotations, thus facilitating annotation acquisition. | https://openaccess.thecvf.com/content/CVPR2022/papers/Koo_PartGlot_Learning_Shape_Part_Segmentation_From_Language_Reference_Games_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Koo_PartGlot_Learning_Shape_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.06390 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Koo_PartGlot_Learning_Shape_Part_Segmentation_From_Language_Reference_Games_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Koo_PartGlot_Learning_Shape_Part_Segmentation_From_Language_Reference_Games_CVPR_2022_paper.html | CVPR 2022 | null |
Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing | Zhuo Wang, Zezheng Wang, Zitong Yu, Weihong Deng, Jiahong Li, Tingting Gao, Zhongyuan Wang | With diverse presentation attacks emerging continually, generalizable face anti-spoofing (FAS) has drawn growing attention. Most existing methods implement domain generalization (DG) on the complete representations. However, different image statistics may have unique properties for the FAS tasks. In this work, we separate the complete representation into content and style ones. A novel Shuffled Style Assembly Network (SSAN) is proposed to extract and reassemble different content and style features for a stylized feature space. Then, to obtain a generalized representation, a contrastive learning strategy is developed to emphasize liveness-related style information while suppress the domain-specific one. Finally, the representations of the correct assemblies are used to distinguish between living and spoofing during the inferring. On the other hand, despite the decent performance, there still exists a gap between academia and industry, due to the difference in data quantity and distribution. Thus, a new large-scale benchmark for FAS is built up to further evaluate the performance of algorithms in reality. Both qualitative and quantitative results on existing and proposed benchmarks demonstrate the effectiveness of our methods. The codes will be available at https://github.com/wangzhuo2019/SSAN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Domain_Generalization_via_Shuffled_Style_Assembly_for_Face_Anti-Spoofing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Domain_Generalization_via_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Domain_Generalization_via_Shuffled_Style_Assembly_for_Face_Anti-Spoofing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Domain_Generalization_via_Shuffled_Style_Assembly_for_Face_Anti-Spoofing_CVPR_2022_paper.html | CVPR 2022 | null |
A Simple Episodic Linear Probe Improves Visual Recognition in the Wild | Yuanzhi Liang, Linchao Zhu, Xiaohan Wang, Yi Yang | Understanding network generalization and feature discrimination is an open research problem in visual recognition. Many studies have been conducted to assess the quality of feature representations. One of the simple strategies is to utilize a linear probing classifier to quantitatively evaluate the class accuracy under the obtained features. The typical linear probe is only applied as a proxy at the inference time, but its efficacy in measuring features' suitability for linear classification is largely neglected in training. In this paper, we propose an episodic linear probing (ELP) classifier to reflect the generalization of visual representations in an online manner. ELP is trained with detached features from the network and re-initialized episodically. It demonstrates the discriminability of the visual representations in training. Then, an ELP-suitable Regularization term (ELP-SR) is introduced to reflect the distances of probability distributions between ELP classifier and the main classifier. ELP-SR leverages a re-scaling factor to regularize each sample in training, which modulates the loss function adaptively and encourages the features to be discriminative and generalized. We observe significant improvements in three real-world visual recognition tasks, including fine-grained visual classification, long-tailed visual recognition, and generic object recognition. The performance gains show the effectiveness of our method in improving network generalization and feature discrimination. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_A_Simple_Episodic_Linear_Probe_Improves_Visual_Recognition_in_the_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_A_Simple_Episodic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_A_Simple_Episodic_Linear_Probe_Improves_Visual_Recognition_in_the_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_A_Simple_Episodic_Linear_Probe_Improves_Visual_Recognition_in_the_CVPR_2022_paper.html | CVPR 2022 | null |
Matching Feature Sets for Few-Shot Image Classification | Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde, Christian Gagné | In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets--namely miniImageNet, tieredImageNet, and CUB--in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Afrasiyabi_Matching_Feature_Sets_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00949 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
DIVeR: Real-Time and Accurate Neural Radiance Fields With Deterministic Integration for Volume Rendering | Liwen Wu, Jae Yong Lee, Anand Bhattad, Yu-Xiong Wang, David Forsyth | DIVeR builds on the key ideas of NeRF and its variants -- density models and volume rendering -- to learn 3D object models that can be rendered realistically from small numbers of images. In contrast to all previous NeRF methods, DIVeR uses deterministic rather than stochastic estimates of the volume rendering integral. DIVeR's representation is a voxel based field of features. To compute the volume rendering integral, a ray is broken into intervals, one per voxel; components of the volume rendering integral are estimated from the features for each interval using an MLP, and the components are aggregated. As a result, DIVeR can render thin translucent structures that are missed by other integrators. Furthermore, DIVeR's representation has semantics that is relatively exposed compared to other such methods -- moving feature vectors around in the voxel space results in natural edits. Extensive qualitative and quantitative comparisons to current state-of-the-art methods show that DIVeR produces models that (1) render at or above state-of-the-art quality, (2) are very small without being baked, (3) render very fast without being baked, and (4) can be edited in natural ways. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_DIVeR_Real-Time_and_Accurate_Neural_Radiance_Fields_With_Deterministic_Integration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_DIVeR_Real-Time_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.10427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_DIVeR_Real-Time_and_Accurate_Neural_Radiance_Fields_With_Deterministic_Integration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_DIVeR_Real-Time_and_Accurate_Neural_Radiance_Fields_With_Deterministic_Integration_CVPR_2022_paper.html | CVPR 2022 | null |
Enhancing Classifier Conservativeness and Robustness by Polynomiality | Ziqi Wang, Marco Loog | We illustrate the detrimental effect, such as overconfident decisions, that exponential behavior can have in methods like classical LDA and logistic regression. We then show how polynomiality can remedy the situation. This, among others, leads purposefully to random-level performance in the tails, away from the bulk of the training data. A directly related, simple, yet important technical novelty we subsequently present is softRmax: a reasoned alternative to the standard softmax function employed in contemporary (deep) neural networks. It is derived through linking the standard softmax to Gaussian class-conditional models, as employed in LDA, and replacing those by a polynomial alternative. We show that two aspects of softRmax, conservativeness and inherent gradient regularization, lead to robustness against adversarial attacks without gradient obfuscation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Enhancing_Classifier_Conservativeness_and_Robustness_by_Polynomiality_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.12693 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Enhancing_Classifier_Conservativeness_and_Robustness_by_Polynomiality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Enhancing_Classifier_Conservativeness_and_Robustness_by_Polynomiality_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization | Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi | Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled data. These tasks are particularly interesting in an unsupervised setting due to the difficulty and cost of obtaining dense image annotations, but existing unsupervised approaches struggle with complex scenes containing multiple objects. Differently from existing methods, which are purely based on deep learning, we take inspiration from traditional spectral segmentation methods by reframing image decomposition as a graph partitioning problem. Specifically, we examine the eigenvectors of the Laplacian of a feature affinity matrix from self-supervised networks. We find that these eigenvectors already decompose an image into meaningful segments, and can be readily used to localize objects in a scene. Furthermore, by clustering the features associated with these segments across a dataset, we can obtain well-delineated, nameable regions, i.e. semantic segmentations. Experiments on complex datasets (Pascal VOC, MS-COCO) demonstrate that our simple spectral method outperforms the state-of-the-art in unsupervised localization and segmentation by a significant margin. Furthermore, our method can be readily used for a variety of complex image editing tasks, such as background removal and compositing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Melas-Kyriazi_Deep_Spectral_Methods_A_Surprisingly_Strong_Baseline_for_Unsupervised_Semantic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Melas-Kyriazi_Deep_Spectral_Methods_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Melas-Kyriazi_Deep_Spectral_Methods_A_Surprisingly_Strong_Baseline_for_Unsupervised_Semantic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Melas-Kyriazi_Deep_Spectral_Methods_A_Surprisingly_Strong_Baseline_for_Unsupervised_Semantic_CVPR_2022_paper.html | CVPR 2022 | null |
OcclusionFusion: Occlusion-Aware Motion Estimation for Real-Time Dynamic 3D Reconstruction | Wenbin Lin, Chengwei Zheng, Jun-Hai Yong, Feng Xu | RGBD-based real-time dynamic 3D reconstruction suffers from inaccurate inter-frame motion estimation as errors may accumulate with online tracking. This problem is even more severe for single-view-based systems due to strong occlusions. Based on these observations, we propose OcclusionFusion, a novel method to calculate occlusion-aware 3D motion to guide the reconstruction. In our technique, the motion of visible regions is first estimated and combined with temporal information to infer the motion of the occluded regions through an LSTM-involved graph neural network. Furthermore, our method computes the confidence of the estimated motion by modeling the network output with a probabilistic model, which alleviates untrustworthy motions and enables robust tracking. Experimental results on public datasets and our own recorded data show that our technique outperforms existing single-view-based real-time methods by a large margin. With the reduction of the motion errors, the proposed technique can handle long and challenging motion sequences. Please check out the project page for sequence results: https://wenbin-lin.github.io/OcclusionFusion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_OcclusionFusion_Occlusion-Aware_Motion_Estimation_for_Real-Time_Dynamic_3D_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_OcclusionFusion_Occlusion-Aware_Motion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07977 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OcclusionFusion_Occlusion-Aware_Motion_Estimation_for_Real-Time_Dynamic_3D_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OcclusionFusion_Occlusion-Aware_Motion_Estimation_for_Real-Time_Dynamic_3D_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
ContIG: Self-Supervised Multimodal Contrastive Learning for Medical Imaging With Genetics | Aiham Taleb, Matthias Kirchler, Remo Monti, Christoph Lippert | High annotation costs are a substantial bottleneck in applying modern deep learning architectures to clinically relevant medical use cases, substantiating the need for novel algorithms to learn from unlabeled data. In this work, we propose ContIG, a self-supervised method that can learn from large datasets of unlabeled medical images and genetic data. Our approach aligns images and several genetic modalities in the feature space using a contrastive loss. We design our method to integrate multiple modalities of each individual person in the same model end-to-end, even when the available modalities vary across individuals. Our procedure outperforms state-of-the-art self-supervised methods on all evaluated downstream benchmark tasks. We also adapt gradient-based explainability algorithms to better understand the learned cross-modal associations between the images and genetic modalities. Finally, we perform genome-wide association studies on the features learned by our models, uncovering interesting relationships between images and genetic data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Taleb_ContIG_Self-Supervised_Multimodal_Contrastive_Learning_for_Medical_Imaging_With_Genetics_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Taleb_ContIG_Self-Supervised_Multimodal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Taleb_ContIG_Self-Supervised_Multimodal_Contrastive_Learning_for_Medical_Imaging_With_Genetics_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Taleb_ContIG_Self-Supervised_Multimodal_Contrastive_Learning_for_Medical_Imaging_With_Genetics_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Domain Generalized Stereo Matching Networks From a Feature Consistency Perspective | Jiawei Zhang, Xiang Wang, Xiao Bai, Chen Wang, Lei Huang, Yimin Chen, Lin Gu, Jun Zhou, Tatsuya Harada, Edwin R. Hancock | Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between matching pixels is a vital factor for promoting the generalization capability of stereo matching networks, which has not been adequately considered. Here we address this issue by proposing a simple pixel-wise contrastive learning across the viewpoints. The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points. A stereo selective whitening loss is further introduced to better preserve the stereo feature consistency across domains, which decorrelates stereo features from stereo viewpoint-specific style information. Counter-intuitively, the generalization of feature consistency between two viewpoints in the same scene translates to the generalization of stereo matching performance to unseen domains. Our method is generic in nature as it can be easily embedded into existing stereo networks and does not require access to the samples in the target domain. When trained on synthetic data and generalized to four real-world testing sets, our method achieves superior performance over several state-of-the-art networks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Revisiting_Domain_Generalized_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.html | CVPR 2022 | null |
MonoScene: Monocular 3D Semantic Scene Completion | Anh-Quan Cao, Raoul de Charette | MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its semantics. Our framework relies on successive 2D and 3D UNets bridged by a novel 2D-3D features projection inspired by optics and introduces a 3D context relation prior to enforce spatio-semantic consistency. Along with architectural contributions, we introduce novel global scene and local frustums losses. Experiments show we outperform the literature on all metrics and datasets while hallucinating plausible scenery even beyond the camera field of view. Our code and trained models are available at https://github.com/cv-rits/MonoScene. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_MonoScene_Monocular_3D_Semantic_Scene_Completion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_MonoScene_Monocular_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00726 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_MonoScene_Monocular_3D_Semantic_Scene_Completion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_MonoScene_Monocular_3D_Semantic_Scene_Completion_CVPR_2022_paper.html | CVPR 2022 | null |
TubeFormer-DeepLab: Video Mask Transformer | Dahun Kim, Jun Xie, Huiyu Wang, Siyuan Qiao, Qihang Yu, Hong-Seok Kim, Hartwig Adam, In So Kweon, Liang-Chieh Chen | We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner. Different video segmentation tasks (e.g., video semantic/instance/panoptic segmentation) are usually considered as distinct problems. State-of-the-art models adopted in the separate communities have diverged, and radically different approaches dominate in each task. By contrast, we make a crucial observation that video segmentation tasks could be generally formulated as the problem of assigning different predicted labels to video tubes (where a tube is obtained by linking segmentation masks along the time axis) and the labels may encode different values depending on the target task. The observation motivates us to develop TubeFormer-DeepLab, a simple and effective video mask transformer model that is widely applicable to multiple video segmentation tasks. TubeFormer-DeepLab directly predicts video tubes with task-specific labels (either pure semantic categories, or both semantic categories and instance identities), which not only significantly simplifies video segmentation models, but also advances state-of-the-art results on multiple video segmentation benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_TubeFormer-DeepLab_Video_Mask_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_TubeFormer-DeepLab_Video_Mask_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_TubeFormer-DeepLab_Video_Mask_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_TubeFormer-DeepLab_Video_Mask_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
XMP-Font: Self-Supervised Cross-Modality Pre-Training for Few-Shot Font Generation | Wei Liu, Fangyue Liu, Fei Ding, Qian He, Zili Yi | Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Few-shot font generation is thus required, as it requires only a few glyph references without fine-tuning during test. Existing methods follow the style-content disentanglement paradigm, and expect novel fonts to be produced by combining the style codes of the reference glyphs and the content representations of the source. However, these few-shot font generation methods either fail to capture content-independent style representations, or employ localized component-wise style representations, which is insufficient to model many Chinese font styles that involve hyper-component features such as inter-component spacing and "connected-stroke". To resolve these drawbacks and make the style representations more reliable, we propose a self-supervised cross-modality pre-training strategy and a cross-modality transformer-based encoder that is conditioned jointly on the glyph image and the corresponding stroke labels. The cross-modality encoder is pre-trained in a self-supervised manner to allow effective capture of cross- and intra-modality correlations, which facilitates the content-style disentanglement and modeling style representations of all scales (stroke-level, components-level and character-level). The pre-trained encoder is then applied to the downstream font generation task without fine-tuning. Experimental comparisons of our method with state-of-the-art methods demonstrate our method successfully transfers styles of all scales. In addition, it only requires one reference glyph and achieves the lowest rate of bad cases in the few-shot font generation task (28% lower than the second best). | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_XMP-Font_Self-Supervised_Cross-Modality_Pre-Training_for_Few-Shot_Font_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_XMP-Font_Self-Supervised_Cross-Modality_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_XMP-Font_Self-Supervised_Cross-Modality_Pre-Training_for_Few-Shot_Font_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_XMP-Font_Self-Supervised_Cross-Modality_Pre-Training_for_Few-Shot_Font_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Disentangling Visual and Written Concepts in CLIP | Joanna Materzyńska, Antonio Torralba, David Bau | The CLIP network measures the similarity between natural text and images; in this work, we investigate the entanglement of the representation of word images and natural images in its image encoder. First, we find that the image encoder has an ability to match word images with natural images of scenes described by those words. This is consistent with previous research that suggests that the meaning and the spelling of a word might be entangled deep within the network. On the other hand, we also find that CLIP has a strong ability to match nonsense words, suggesting that processing of letters is separated from processing of their meaning. To explicitly determine whether the spelling capability of CLIP is separable, we devise a procedure for identifying representation subspaces that selectively isolate or eliminate spelling capabilities. We benchmark our methods against a range of retrieval tasks, and we also test them by measuring the appearance of text in CLIP-guided generated images. We find that our methods are able to cleanly separate spelling capabilities of CLIP from the visual processing of natural images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Materzynska_Disentangling_Visual_and_Written_Concepts_in_CLIP_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Materzynska_Disentangling_Visual_and_Written_Concepts_in_CLIP_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Materzynska_Disentangling_Visual_and_Written_Concepts_in_CLIP_CVPR_2022_paper.html | CVPR 2022 | null |
Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction | Christiane Sommer, Lu Sang, David Schubert, Daniel Cremers | We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations. By storing at every voxel both the signed distance field as well as its gradient vector field, we enhance the capability of implicit representations with approaches originally formulated for explicit surfaces. As concrete examples, we show that (1) the Gradient-SDF allows us to perform direct SDF tracking from depth images, using efficient storage schemes like hash maps, and that (2) the Gradient-SDF representation enables us to perform photometric bundle adjustment directly in a voxel representation (without transforming into a point cloud or mesh), naturally a fully implicit optimization of geometry and camera poses and easy geometry upsampling. Experimental results confirm that this leads to significantly sharper reconstructions. Since the overall SDF voxel structure is still respected, the proposed Gradient-SDF is equally suited for (GPU) parallelization as related approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sommer_Gradient-SDF_A_Semi-Implicit_Surface_Representation_for_3D_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sommer_Gradient-SDF_A_Semi-Implicit_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sommer_Gradient-SDF_A_Semi-Implicit_Surface_Representation_for_3D_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sommer_Gradient-SDF_A_Semi-Implicit_Surface_Representation_for_3D_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
Bilateral Video Magnification Filter | Shoichiro Takeda, Kenta Niwa, Mariko Isogawa, Shinya Shimizu, Kazuki Okami, Yushi Aono | Eulerian video magnification (EVM) has progressed to magnify subtle motions with a target frequency even under the presence of large motions of objects. However, existing EVM methods often fail to produce desirable results in real videos due to (1) mis-extracting subtle motions with a non-target frequency and (2) collapsing results when large de/acceleration motions occur (e.g., objects suddenly start, stop, or change direction). To enhance EVM performance on real videos, this paper proposes a bilateral video magnification filter (BVMF) that offers simple yet robust temporal filtering. BVMF has two kernels; (I) one kernel performs temporal bandpass filtering via a Laplacian of Gaussian whose passband peaks at the target frequency with unity gain and (II) the other kernel excludes large motions outside the magnitude of interest by Gaussian filtering on the intensity of the input signal via the Fourier shift theorem. Thus, BVMF extracts only subtle motions with the target frequency while excluding large motions outside the magnitude of interest, regardless of motion dynamics. In addition, BVMF runs the two kernels in the temporal and intensity domains simultaneously like the bilateral filter does in the spatial and intensity domains. This simplifies implementation and, as a secondary effect, keeps the memory usage low. Experiments conducted on synthetic and real videos show that BVMF outperforms state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Takeda_Bilateral_Video_Magnification_Filter_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Takeda_Bilateral_Video_Magnification_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Takeda_Bilateral_Video_Magnification_Filter_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Takeda_Bilateral_Video_Magnification_Filter_CVPR_2022_paper.html | CVPR 2022 | null |
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition | Yulin Wang, Yang Yue, Yuanze Lin, Haojun Jiang, Zihang Lai, Victor Kulikov, Nikita Orlov, Humphrey Shi, Gao Huang | Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy. As a representative work, the adaptive focus method (AdaFocus) has achieved a favorable trade-off between accuracy and inference speed by dynamically identifying and attending to the informative regions in each video frame. However, AdaFocus requires a complicated three-stage training pipeline (involving reinforcement learning), leading to slow convergence and is unfriendly to practitioners. This work reformulates the training of AdaFocus as a simple one-stage algorithm by introducing a differentiable interpolation-based patch selection operation, enabling efficient end-to-end optimization. We further present an improved training scheme to address the issues introduced by the one-stage formulation, including the lack of supervision, input diversity and training stability. Moreover, a conditional-exit technique is proposed to perform temporal adaptive computation on top of AdaFocus without additional training. Extensive experiments on six benchmark datasets (i.e., ActivityNet, FCVID, Mini-Kinetics, Something-Something V1&V2, and Jester) demonstrate that our model significantly outperforms the original AdaFocus and other competitive baselines, while being considerably more simple and efficient to train. Code is available at https://github.com/ LeapLabTHU/AdaFocusV2. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_AdaFocus_V2_End-to-End_Training_of_Spatial_Dynamic_Networks_for_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_AdaFocus_V2_End-to-End_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.14238 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_AdaFocus_V2_End-to-End_Training_of_Spatial_Dynamic_Networks_for_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_AdaFocus_V2_End-to-End_Training_of_Spatial_Dynamic_Networks_for_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Localization Distillation for Dense Object Detection | Zhaohui Zheng, Rongguang Ye, Ping Wang, Dongwei Ren, Wangmeng Zuo, Qibin Hou, Ming-Ming Cheng | Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of logit mimicking on classification due to the inefficiency in distilling localization information. In this paper, by reformulating the knowledge distillation process on localization, we present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Moreover, we also heuristically introduce the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and, localization knowledge distillation is more important and efficient than semantic knowledge for distilling object detectors. Our distillation scheme is simple as well as effective and can be easily applied to different dense object detectors. Experiments show that our LD can boost the AP score of GFocal-ResNet-50 with a single-scale 1x training schedule from 40.1 to 42.1 on the COCO benchmark without any sacrifice on the inference speed. Our source code and pretrained models will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Localization_Distillation_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2102.12252 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Localization_Distillation_for_Dense_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
What's in Your Hands? 3D Reconstruction of Generic Objects in Hands | Yufei Ye, Abhinav Gupta, Shubham Tulsiani | Our work aims to reconstruct hand-held objects given a single RGB image. In contrast to prior works that typically assume known 3D templates and reduce the problem to 3D pose estimation, our work reconstructs generic hand-held objects without knowing their 3D templates. Our key insight is that hand articulation is highly predictive of the object shape, and we propose an approach that conditionally reconstructs the object based on the articulation and the visual input. Given an image depicting a hand-held object, we first use off-the-shelf systems to estimate the underlying hand pose and then infer the object shape in a normalized hand-centric coordinate frame. We parameterized the object by signed distance which is inferred by an implicit network that leverages the information from both visual feature and articulation-aware coordinates to process a query point. We perform experiments across three datasets and show that our method consistently outperforms baselines and is able to reconstruct a diverse set of objects. We analyze the benefits and robustness of explicit articulation conditioning and also show that this allows the hand pose estimation to further improve in test-time optimization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Whats_in_Your_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Whats_in_Your_Hands_3D_Reconstruction_of_Generic_Objects_in_CVPR_2022_paper.html | CVPR 2022 | null |
Continuous Scene Representations for Embodied AI | Samir Yitzhak Gadre, Kiana Ehsani, Shuran Song, Roozbeh Mottaghi | We propose Continuous Scene Representations (CSR), a scene representation constructed by an embodied agent navigating within a space, where objects and their relationships are modeled by continuous valued embeddings. Our method captures feature relationships between objects, composes them into a graph structure on-the-fly, and situates an embodied agent within the representation. Our key insight is to embed pair-wise relationships between objects in a latent space. This allows for a richer representation compared to discrete relations (e.g., [support], [next-to]) commonly used for building scene representations. CSR can track objects as the agent moves in a scene, update the representation accordingly, and detect changes in room configurations. Using CSR, we outperform state-of-the-art approaches for the challenging downstream task of visual room rearrangement, without any task specific training. Moreover, we show the learned embeddings capture salient spatial details of the scene and show applicability to real world data. A summery video and code is available at https://prior.allenai.org/projects/csr. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gadre_Continuous_Scene_Representations_for_Embodied_AI_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gadre_Continuous_Scene_Representations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gadre_Continuous_Scene_Representations_for_Embodied_AI_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gadre_Continuous_Scene_Representations_for_Embodied_AI_CVPR_2022_paper.html | CVPR 2022 | null |
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds | Chaoda Zheng, Xu Yan, Haiming Zhang, Baoyuan Wang, Shenghui Cheng, Shuguang Cui, Zhen Li | 3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous methods greatly overlook the critical motion clues among targets. In this work, beyond 3D Siamese tracking, we introduce a motion-centric paradigm to handle 3D SOT from a new perspective. Following this paradigm, we propose a matching-free two-stage tracker M^2-Track. At the 1^st-stage, M^2-Track localizes the target within successive frames via motion transformation. Then it refines the target box through motion-assisted shape completion at the 2^nd-stage. Extensive experiments confirm that M^2-Track significantly outperforms previous state-of-the-arts on three large-scale datasets while running at 57FPS ( 8%, 17%, and 22%) precision gains on KITTI, NuScenes, and Waymo Open Dataset respectively). Further analysis verifies each component's effectiveness and shows the motion-centric paradigm's promising potential when combined with appearance matching. Code will be made available at https://github.com/Ghostish/Open3DSOT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Beyond_3D_Siamese_Tracking_A_Motion-Centric_Paradigm_for_3D_Single_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Beyond_3D_Siamese_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01730 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Beyond_3D_Siamese_Tracking_A_Motion-Centric_Paradigm_for_3D_Single_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Beyond_3D_Siamese_Tracking_A_Motion-Centric_Paradigm_for_3D_Single_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Mean Discrepancy for Efficient Out-of-Distribution Detection | Xin Dong, Junfeng Guo, Ang Li, Wei-Te Ting, Cong Liu, H.T. Kung | Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training set, and optimization objectives. Deviating from existing work, we have a simple hypothesis that standard off-the-shelf models may already contain sufficient information about the training set distribution which can be leveraged for reliable OOD detection. Our empirical study on validating this hypothesis, which measures the model activation's mean for OOD and in-distribution (ID) mini-batches, surprisingly finds that activation means of OOD mini-batches consistently deviate more from those of the training data. In addition, training data's activation means can be computed offline efficiently or retrieved from batch normalization layers as a "free lunch". Based upon this observation, we propose a novel metric called Neural Mean Discrepancy (NMD), which compares neural means of the input examples and training data. Leveraging the simplicity of NMD, we propose an efficient OOD detector that computes neural means by a standard forward pass followed by a lightweight classifier. Extensive experiments show that NMD outperforms state-of-the-art OOD approaches across multiple datasets and model architectures in terms of both detection accuracy and computational cost. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Neural_Mean_Discrepancy_for_Efficient_Out-of-Distribution_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Neural_Mean_Discrepancy_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.11408 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Neural_Mean_Discrepancy_for_Efficient_Out-of-Distribution_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Neural_Mean_Discrepancy_for_Efficient_Out-of-Distribution_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Non-Probability Sampling Network for Stochastic Human Trajectory Prediction | Inhwan Bae, Jin-Hwi Park, Hae-Gon Jeon | Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to infer a finite set of future trajectories. The inferred trajectories are based on observation paths and the latent vectors of potential decisions of pedestrians in the inference step. However, stochastic approaches provide varying results for the same data and parameter settings, due to the random sampling of the latent vector. In this paper, we analyze the problem by reconstructing and comparing probabilistic distributions from prediction samples and socially-acceptable paths, respectively. Through this analysis, we observe that the inferences of all stochastic models are biased toward the random sampling, and fail to generate a set of realistic paths from finite samples. The problem cannot be resolved unless an infinite number of samples is available, which is infeasible in practice. We introduce that the Quasi-Monte Carlo (QMC) method, ensuring uniform coverage on the sampling space, as an alternative to the conventional random sampling. With the same finite number of samples, the QMC improves all the multimodal prediction results. We take an additional step ahead by incorporating a learnable sampling network into the existing networks for trajectory prediction. For this purpose, we propose the Non-Probability Sampling Network (NPSN), a very small network ( 5K parameters) that generates purposive sample sequences using the past paths of pedestrians and their social interactions. Extensive experiments confirm that NPSN can significantly improve both the prediction accuracy (up to 60%) and reliability of the public pedestrian trajectory prediction benchmark. Code is publicly available at https://github.com/inhwanbae/NPSN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bae_Non-Probability_Sampling_Network_for_Stochastic_Human_Trajectory_Prediction_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13471 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bae_Non-Probability_Sampling_Network_for_Stochastic_Human_Trajectory_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bae_Non-Probability_Sampling_Network_for_Stochastic_Human_Trajectory_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Marginal Contrastive Correspondence for Guided Image Generation | Fangneng Zhan, Yingchen Yu, Rongliang Wu, Jiahui Zhang, Shijian Lu, Changgong Zhang | Exemplar-based image translation establishes dense correspondences between a conditional input and an exemplar (from two different domains) for leveraging detailed exemplar styles to achieve realistic image translation. Existing work builds the cross-domain correspondences implicitly by minimizing feature-wise distances across the two domains. Without explicit exploitation of domain-invariant features, this approach may not reduce the domain gap effectively which often leads to sub-optimal correspondences and image translation. We design a Marginal Contrastive Learning Network (MCL-Net) that explores contrastive learning to learn domain-invariant features for realistic exemplar-based image translation. Specifically, we design an innovative marginal contrastive loss that guides to establish dense correspondences explicitly. Nevertheless, building correspondence with domain-invariant semantics alone may impair the texture patterns and lead to degraded texture generation. We thus design a Self-Correlation Map (SCM) that incorporates scene structures as auxiliary information which improves the built correspondences substantially. Quantitative and qualitative experiments on multifarious image translation tasks show that the proposed method outperforms the state-of-the-art consistently. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.00442 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhan_Marginal_Contrastive_Correspondence_for_Guided_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Complex Backdoor Detection by Symmetric Feature Differencing | Yingqi Liu, Guangyu Shen, Guanhong Tao, Zhenting Wang, Shiqing Ma, Xiangyu Zhang | Many existing backdoor scanners work by finding a small and fixed trigger. However, advanced attacks have large and pervasive triggers, rendering existing scanners less effective. We develop a new detection method. It first uses a trigger inversion technique to generate triggers, namely, universal input patterns flipping victim class samples to a target class. It then checks if any such trigger is composed of features that are not natural distinctive features between the victim and target classes. It is based on a novel symmetric feature differencing method that identifies features separating two sets of samples (e.g., from two respective classes). We evaluate the technique on a number of advanced attacks including composite attack, reflection attack, hidden attack, filter attack, and also on the traditional patch attack. The evaluation is on thousands of models, including both clean and trojaned models, with various architectures. We compare with three state-of-the-art scanners. Our technique can achieve 80-88% accuracy while the baselines can only achieve 50-70% on complex attacks. Our results on the TrojAI competition rounds 2-4, which have patch backdoors and filter backdoors, show that existing scanners may produce hundreds of false positives (i.e., clean models recognized as trojaned), while our technique removes 78-100% of them with a small increase of false negatives by 0-30%, leading to 17-41% overall accuracy improvement. This allows us to achieve top performance on the leaderboard. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Complex_Backdoor_Detection_by_Symmetric_Feature_Differencing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Complex_Backdoor_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Complex_Backdoor_Detection_by_Symmetric_Feature_Differencing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Complex_Backdoor_Detection_by_Symmetric_Feature_Differencing_CVPR_2022_paper.html | CVPR 2022 | null |
Time Lens++: Event-Based Frame Interpolation With Parametric Non-Linear Flow and Multi-Scale Fusion | Stepan Tulyakov, Alfredo Bochicchio, Daniel Gehrig, Stamatios Georgoulis, Yuanyou Li, Davide Scaramuzza | Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency. However, current methods still suffer from (i) brittle image-level fusion of complementary interpolation results, that fails in the presence of artifacts in the fused image, (ii) potentially temporally inconsistent and inefficient motion estimation procedures, that run for every inserted frame and (iii) low contrast regions that do not trigger events, and thus cause events-only motion estimation to generate artifacts. Moreover, previous methods were only tested on datasets consisting of planar and far-away scenes, which do not capture the full complexity of the real world. In this work, we address the above problems by introducing multi-scale feature-level fusion and computing one-shot non-linear inter-frame motion---which can be efficiently sampled for image warping---from events and images. We also collect the first large-scale events and frames dataset consisting of more than 100 challenging scenes with depth variations, captured with a new experimental setup based on a beamsplitter. We show that our method improves the reconstruction quality by up to 0.2 dB in terms of PSNR and by up to 15% in LPIPS score. Code and dataset will be released upon acceptance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tulyakov_Time_Lens_Event-Based_Frame_Interpolation_With_Parametric_Non-Linear_Flow_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tulyakov_Time_Lens_Event-Based_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tulyakov_Time_Lens_Event-Based_Frame_Interpolation_With_Parametric_Non-Linear_Flow_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tulyakov_Time_Lens_Event-Based_Frame_Interpolation_With_Parametric_Non-Linear_Flow_and_CVPR_2022_paper.html | CVPR 2022 | null |
ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | Jingtao Li, Adnan Siraj Rakin, Xing Chen, Zhezhi He, Deliang Fan, Chaitali Chakrabarti | This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational load at the client end, it opens itself to reconstruction of raw data from intermediate activation by the server. Existing works on protecting SFL only consider inference and do not handle attacks during training. So we propose ResSFL, a Split Federated Learning Framework that is designed to be MI-resistant during training. It is based on deriving a resistant feature extractor via attacker-aware training, and using this extractor to initialize the client-side model prior to standard SFL training. Such a method helps in reducing the computational complexity due to use of strong inversion model in client-side adversarial training as well as vulnerability of attacks launched in early training epochs. On CIFAR-100 dataset, our proposed framework successfully mitigates MI attack on a VGG-11 model with a high reconstruction Mean-Square-Error of 0.050 compared to 0.005 obtained by the baseline system. The framework achieves 67.5% accuracy (only 1% accuracy drop) with very low computation overhead. Code is released at: https://github.com/zlijingtao/ResSFL | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_ResSFL_A_Resistance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.04007 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks | Yufei Guo, Xinyi Tong, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Zhe Ma, Xuhui Huang | The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the synaptic activity of biological neurons, which transmits binary spike signals between network units when the membrane potential exceeds the firing threshold. This bio-mimetic mechanism of SNN appears energy-efficiency with its power sparsity and asynchronous operations on spike events. Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence. Such undesired shifts would prevent the SNN from performing well and going deep. To tackle these problems, we attempt to rectify the membrane potential distribution (MPD) by designing a novel distribution loss, MPD-Loss, which can explicitly penalize the undesired shifts without introducing any additional operations in the inference phase. Moreover, the proposed method can also mitigate the quantization error in SNNs, which is usually ignored in other works. Experimental results demonstrate that the proposed method can directly train a deeper, larger and better performing SNN within fewer timesteps. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_RecDis-SNN_Rectifying_Membrane_Potential_Distribution_for_Directly_Training_Spiking_Neural_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_RecDis-SNN_Rectifying_Membrane_Potential_Distribution_for_Directly_Training_Spiking_Neural_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_RecDis-SNN_Rectifying_Membrane_Potential_Distribution_for_Directly_Training_Spiking_Neural_CVPR_2022_paper.html | CVPR 2022 | null |
Human-Aware Object Placement for Visual Environment Reconstruction | Hongwei Yi, Chun-Hao P. Huang, Dimitrios Tzionas, Muhammed Kocabas, Mohamed Hassan, Siyu Tang, Justus Thies, Michael J. Black | Humans are in constant contact with the world as they move through it and interact with it. This contact is a vital source of information for understanding 3D humans, 3D scenes, and the interactions between them. In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video. Our key idea is that, as a person moves through a scene and interacts with it, we accumulate HSIs across multiple input images, and use these in optimizing the 3D scene to reconstruct a consistent, physically plausible, 3D scene layout. Our optimization-based approach exploits three types of HSI constraints: (1) humans who move in a scene are occluded by, or occlude, objects, thus constraining the depth ordering of the objects, (2) humans move through free space and do not interpenetrate objects, (3) when humans and objects are in contact, the contact surfaces occupy the same place in space. Using these constraints in an optimization formulation across all observations, we significantly improve 3D scene layout reconstruction. Furthermore, we show that our scene reconstruction can be used to refine the initial 3D human pose and shape (HPS) estimation. We evaluate the 3D scene layout reconstruction and HPS estimates qualitatively and quantitatively using the PROX and PiGraphs datasets. The code and data are available for research purposes at https://mover.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yi_Human-Aware_Object_Placement_for_Visual_Environment_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yi_Human-Aware_Object_Placement_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yi_Human-Aware_Object_Placement_for_Visual_Environment_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yi_Human-Aware_Object_Placement_for_Visual_Environment_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval | Satya Krishna Gorti, Noël Vouitsis, Junwei Ma, Keyvan Golestan, Maksims Volkovs, Animesh Garg, Guangwei Yu | In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However, videos inherently express a much wider gamut of information than texts. Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos. Therefore, for a given text, a retrieval model should focus on the text's most semantically similar video sub-regions to make a more relevant comparison. Yet, most existing works aggregate entire videos without directly considering text. Common text-agnostic aggregations schemes include mean-pooling or self-attention over the frames, but these are likely to encode misleading visual information not described in the given text. To address this, we propose a cross-modal attention model called X-Pool that reasons between a text and the frames of a video. Our core mechanism is a scaled dot product attention for a text to attend to its most semantically similar frames. We then generate an aggregated video representation conditioned on the text's attention weights over the frames. We evaluate our method on three benchmark datasets of MSR-VTT, MSVD and LSMDC, achieving new state-of-the-art results by up to 12% in relative improvement in Recall@1. Our findings thereby highlight the importance of joint text-video reasoning to extract important visual cues according to text. Full code and demo can be found at: https://layer6ai-labs.github.io/xpool/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Gorti_X-Pool_Cross-Modal_Language-Video_Attention_for_Text-Video_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gorti_X-Pool_Cross-Modal_Language-Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gorti_X-Pool_Cross-Modal_Language-Video_Attention_for_Text-Video_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gorti_X-Pool_Cross-Modal_Language-Video_Attention_for_Text-Video_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
Learning of Global Objective for Network Flow in Multi-Object Tracking | Shuai Li, Yu Kong, Hamid Rezatofighi | This paper concerns the problem of multi-object tracking based on the min-cost flow (MCF) formulation, which is conventionally studied as an instance of linear program. Given its computationally tractable inference, the success of MCF tracking largely relies on the learned cost function of underlying linear program. Most previous studies focus on learning the cost function by only taking into account two frames during training, therefore the learned cost function is sub-optimal for MCF where a multi-frame data association must be considered during inference. In order to address this problem, in this paper we propose a novel differentiable framework that ties training and inference together during learning by solving a bi-level optimization problem, where the lower-level solves a linear program and the upper-level contains a loss function that incorporates global tracking result. By back-propagating the loss through differentiable layers via gradient descent, the globally parameterized cost function is explicitly learned and regularized. With this approach, we are able to learn a better objective for global MCF tracking. As a result, we achieve competitive performances compared to the current state-of-the-art methods on the popular multi-object tracking benchmarks such as MOT16, MOT17 and MOT20 | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Learning_of_Global_Objective_for_Network_Flow_in_Multi-Object_Tracking_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16210 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_of_Global_Objective_for_Network_Flow_in_Multi-Object_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Learning_of_Global_Objective_for_Network_Flow_in_Multi-Object_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Weakly-Supervised Text Spotting Using a Multi-Task Transformer | Yair Kittenplon, Inbal Lavi, Sharon Fogel, Yarin Bar, R. Manmatha, Pietro Perona | Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kittenplon_Towards_Weakly-Supervised_Text_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.05508 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kittenplon_Towards_Weakly-Supervised_Text_Spotting_Using_a_Multi-Task_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Gated2Gated: Self-Supervised Depth Estimation From Gated Images | Amanpreet Walia, Stefanie Walz, Mario Bijelic, Fahim Mannan, Frank Julca-Aguilar, Michael Langer, Werner Ritter, Felix Heide | Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially scanning a scene and directly recording depth via the photon time-of-flight, as in pulsed LiDAR sensors, gated imagers encode depth in the relative intensity of a handful of gated slices, captured at megapixel resolution. Although existing methods have shown that it is possible to decode high-resolution depth from such measurements, these methods require synchronized and calibrated LiDAR to supervise the gated depth decoder - prohibiting fast adoption across geographies, training on large unpaired datasets, and exploring alternative applications outside of automotive use cases. In this work, we fill this gap and propose an entirely self-supervised depth estimation method that uses gated intensity profiles and temporal consistency as a training signal. The proposed model is trained end-to-end from gated video sequences, does not require LiDAR or RGB data, and learns to estimate absolute depth values. We take gated slices as input and disentangle the estimation of the scene albedo, depth, and ambient light, which are then used to learn to reconstruct the input slices through a cyclic loss. We rely on temporal consistency between a given frame and neighboring gated slices to estimate depth in regions with shadows and reflections. We experimentally validate that the proposed approach outperforms existing supervised and self-supervised depth estimation methods based on monocular RGB and stereo images, as well as supervised methods based on gated images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Walia_Gated2Gated_Self-Supervised_Depth_Estimation_From_Gated_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Walia_Gated2Gated_Self-Supervised_Depth_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02416 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Walia_Gated2Gated_Self-Supervised_Depth_Estimation_From_Gated_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Walia_Gated2Gated_Self-Supervised_Depth_Estimation_From_Gated_Images_CVPR_2022_paper.html | CVPR 2022 | null |
RAMA: A Rapid Multicut Algorithm on GPU | Ahmed Abbas, Paul Swoboda | We propose a highly parallel primal-dual algorithm for the multicut (a.k.a. correlation clustering) problem, a classical graph clustering problem widely used in machine learning and computer vision. Our algorithm consists of three steps executed recursively: (1) Finding conflicted cycles that correspond to violated inequalities of the underlying multicut relaxation, (2) Performing message passing between the edges and cycles to optimize the Lagrange relaxation coming from the found violated cycles producing reduced costs and (3) Contracting edges with high reduced costs through matrix-matrix multiplications. Our algorithm produces primal solutions and lower bounds that estimate the distance to optimum. We implement our algorithm on GPUs and show resulting one to two orders-of-magnitudes improvements in execution speed without sacrificing solution quality compared to traditional sequential algorithms that run on CPUs. We can solve very large scale benchmark problems with up to O(10^8) variables in a few seconds with small primal-dual gaps. Our code is available at https://github.com/pawelswoboda/RAMA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Abbas_RAMA_A_Rapid_Multicut_Algorithm_on_GPU_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Abbas_RAMA_A_Rapid_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.01838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Abbas_RAMA_A_Rapid_Multicut_Algorithm_on_GPU_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Abbas_RAMA_A_Rapid_Multicut_Algorithm_on_GPU_CVPR_2022_paper.html | CVPR 2022 | null |
Adversarial Parametric Pose Prior | Andrey Davydov, Anastasia Remizova, Victor Constantin, Sina Honari, Mathieu Salzmann, Pascal Fua | The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is under-constrained and may yield invalid results. We propose learning a prior that restricts the SMPL parameters to values that produce realistic poses via adversarial training. We show that our learned prior covers the diversity of the real-data distribution, facilitates optimization for 3D reconstruction from 2D keypoints, and yields better pose estimates when used for regression from images. For all these tasks, it outperforms the state-of-the-art VAE-based approach to constraining the SMPL parameters. The code will be made available at https://github.com/cvlab-epfl/adv_param_pose_prior. | https://openaccess.thecvf.com/content/CVPR2022/papers/Davydov_Adversarial_Parametric_Pose_Prior_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Davydov_Adversarial_Parametric_Pose_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04203 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Davydov_Adversarial_Parametric_Pose_Prior_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Davydov_Adversarial_Parametric_Pose_Prior_CVPR_2022_paper.html | CVPR 2022 | null |
DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning | Zhen Zhao, Luping Zhou, Yue Duan, Lei Wang, Lei Qi, Yinghuan Shi | Consistency-based Semi-supervised learning (SSL) has achieved promising performance recently. However, the success largely depends on the assumption that the labeled and unlabeled data share an identical class distribution, which is hard to meet in real practice. The distribution mismatch between the labeled and unlabeled sets can cause severe bias in the pseudo-labels of SSL, resulting in significant performance degradation. To bridge this gap, we put forward a new SSL learning framework, named Distribution Consistency SSL (DC-SSL), which rectifies the pseudo-labels from a distribution perspective. The basic idea is to directly estimate a reference class distribution (RCD), which is regarded as a surrogate of the ground truth class distribution about the unlabeled data, and then improve the pseudo-labels by encouraging the predicted class distribution (PCD) of the unlabeled data to approach RCD gradually. To this end, this paper revisits the Exponentially Moving Average (EMA) model and utilizes it to estimate RCD in an iteratively improved manner, which is achieved with a momentum-update scheme throughout the training procedure. On top of this, two strategies are proposed for RCD to rectify the pseudo-label prediction, respectively. They correspond to an efficient training-free scheme and a training-based alternative that generates more accurate and reliable predictions. DC-SSL is evaluated on multiple SSL benchmarks and demonstrates remarkable performance improvement over competitive methods under matched- and mismatched-distribution scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_DC-SSL_Addressing_Mismatched_Class_Distribution_in_Semi-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_DC-SSL_Addressing_Mismatched_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DC-SSL_Addressing_Mismatched_Class_Distribution_in_Semi-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_DC-SSL_Addressing_Mismatched_Class_Distribution_in_Semi-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Mask Transfiner for High-Quality Instance Segmentation | Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu | Two-stage and query-based instance segmentation methods have achieved remarkable results. However, their segmented masks are still very coarse. In this paper, we present Mask Transfiner for high-quality and efficient instance segmentation. Instead of operating on regular dense tensors, our Mask Transfiner decomposes and represents the image regions as a quadtree. Our transformer-based approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. While these sparse pixels only constitute a small proportion of the total number, they are critical to the final mask quality. This allows Mask Transfiner to predict highly accurate instance masks, at a low computational cost. Extensive experiments demonstrate that Mask Transfiner outperforms current instance segmentation methods on three popular benchmarks, significantly improving both two-stage and query-based frameworks by a large margin of +3.0 mask AP on COCO and BDD100K, and +6.6 boundary AP on Cityscapes. Our code and trained models are available at https://github.com/SysCV/transfiner. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ke_Mask_Transfiner_for_High-Quality_Instance_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ke_Mask_Transfiner_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13673 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ke_Mask_Transfiner_for_High-Quality_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ke_Mask_Transfiner_for_High-Quality_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Reconstruction-Classification Learning for Face Forgery Detection | Junyi Cao, Chao Ma, Taiping Yao, Shen Chen, Shouhong Ding, Xiaokang Yang | Existing face forgery detectors mainly focus on specific forgery patterns like noise characteristics, local textures, or frequency statistics for forgery detection. This causes specialization of learned representations to known forgery patterns presented in the training set, and makes it difficult to detect forgeries with unknown patterns. In this paper, from a new perspective, we propose a forgery detection framework emphasizing the common compact representations of genuine faces based on reconstruction-classification learning. Reconstruction learning over real images enhances the learned representations to be aware of forgery patterns that are even unknown, while classification learning takes the charge of mining the essential discrepancy between real and fake images, facilitating the understanding of forgeries. To achieve better representations, instead of only using the encoder in reconstruction learning, we build bipartite graphs over the encoder and decoder features in a multi-scale fashion. We further exploit the reconstruction difference as guidance of forgery traces on the graph output as the final representation, which is fed into the classifier for forgery detection. The reconstruction and classification learning is optimized end-to-end. Extensive experiments on large-scale benchmark datasets demonstrate the superiority of the proposed method over state of the arts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_End-to-End_Reconstruction-Classification_Learning_for_Face_Forgery_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_End-to-End_Reconstruction-Classification_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_End-to-End_Reconstruction-Classification_Learning_for_Face_Forgery_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_End-to-End_Reconstruction-Classification_Learning_for_Face_Forgery_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
It Is Okay To Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection | Youssef Mohamed, Faizan Farooq Khan, Kilichbek Haydarov, Mohamed Elhoseiny | Datasets that capture the connection between vision, language, and affection are limited, causing a lack of understanding of the emotional aspect of human intelligence. As a step in this direction, the ArtEmis dataset was recently introduced as a large-scale dataset of emotional reactions to images along with language explanations of these chosen emotions. We observed a significant emotional bias towards instance-rich emotions, making trained neural speakers less accurate in describing under-represented emotions. We show that collecting new data, in the same way, is not effective in mitigating this emotional bias. To remedy this problem, we propose a contrastive data collection approach to balance ArtEmis with a new complementary dataset such that a pair of similar images have contrasting emotions (one positive and one negative). We collected 260,533 instances using the proposed method, we combine them with ArtEmis, creating a second iteration of the dataset. The new combined dataset, dubbed ArtEmis v2.0, has a balanced distribution of emotions with explanations revealing more fine details in the associated painting. Our experiments show that neural speakers trained on the new dataset improve CIDEr and METEOR evaluation metrics by 20% and 7%, respectively, compared to the biased dataset. Finally, we also show that the performance per emotion of neural speakers is improved across all the emotion categories, significantly on under-represented emotions. The collected dataset and code are available at https://artemisdataset-v2.org. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mohamed_It_Is_Okay_To_Not_Be_Okay_Overcoming_Emotional_Bias_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mohamed_It_Is_Okay_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07660 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mohamed_It_Is_Okay_To_Not_Be_Okay_Overcoming_Emotional_Bias_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mohamed_It_Is_Okay_To_Not_Be_Okay_Overcoming_Emotional_Bias_CVPR_2022_paper.html | CVPR 2022 | null |
Transferability Metrics for Selecting Source Model Ensembles | Andrea Agostinelli, Jasper Uijlings, Thomas Mensink, Vittorio Ferrari | We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the target training set, yields the best performance on the target test set. Since fine-tuning all possible ensembles is computationally prohibitive, we aim at predicting performance on the target dataset using a computationally efficient transferability metric. We propose several new transferability metrics designed for this task and evaluate them in a challenging and realistic transfer learning setup for semantic segmentation: we create a large and diverse pool of source models by considering 17 source datasets covering a wide variety of image domain, two different architectures, and two pre-training schemes. Given this pool, we then automatically select a subset to form an ensemble performing well on a given target dataset. We compare the ensemble selected by our method to two baselines which select a single source model, either (1) from the same pool as our method; or (2) from a pool containing large source models, each with similar capacity as an ensemble. Averaged over 17 target datasets, we outperform these baselines by 6.0% and 2.5% relative mean IoU, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Agostinelli_Transferability_Metrics_for_Selecting_Source_Model_Ensembles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Agostinelli_Transferability_Metrics_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13011 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Agostinelli_Transferability_Metrics_for_Selecting_Source_Model_Ensembles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Agostinelli_Transferability_Metrics_for_Selecting_Source_Model_Ensembles_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Global Shutter: Learn To Restore Video From a Rolling Shutter Camera With Global Reset Feature | Zhixiang Wang, Xiang Ji, Jia-Bin Huang, Shin'ichi Satoh, Xiao Zhou, Yinqiang Zheng | Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera and object undergo motion during capture. Extensive researches have been conducted on correcting RS distortions. However, most of the existing work relies heavily on the prior assumptions of scenes or motions. Besides, the motion estimation steps are either oversimplified or computationally inefficient due to the heavy flow warping, limiting their applicability. In this paper, we investigate using rolling shutter with a global reset feature (RSGR) to restore clean global shutter (GS) videos. This feature enables us to turn the rectification problem into a deblur-like one, getting rid of inaccurate and costly explicit motion estimation. First, we build an optic system that captures paired RSGR/GS videos. Second, we develop a novel algorithm incorporating spatial and temporal designs to correct the spatial-varying RSGR distortion. Third, we demonstrate that existing image-to-image translation algorithms can recover clean GS videos from distorted RSGR inputs, yet our algorithm achieves the best performance with the specific designs. Our rendered results are not only visually appealing but also beneficial to downstream tasks. Compared to the state-of-the-art RS solution, our RSGR solution is superior in both effectiveness and efficiency. Considering it is easy to realize without changing the hardware, we believe our RSGR solution can potentially replace the RS solution in taking distortion-free videos with low noise and low budget. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Neural_Global_Shutter_Learn_To_Restore_Video_From_a_Rolling_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.00974 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Neural_Global_Shutter_Learn_To_Restore_Video_From_a_Rolling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Neural_Global_Shutter_Learn_To_Restore_Video_From_a_Rolling_CVPR_2022_paper.html | CVPR 2022 | null |
DiRA: Discriminative, Restorative, and Adversarial Learning for Self-Supervised Medical Image Analysis | Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang | Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging. Existing efforts, however, omit their synergistic effects on each other in a ternary setup, which, we envision, can significantly benefit deep semantic representation learning. To realize this vision, we have developed DiRA, the first framework that unites discriminative, restorative, and adversarial learning in a unified manner to collaboratively glean complementary visual information from unlabeled medical images for fine-grained semantic representation learning. Our extensive experiments demonstrate that DiRA (1) encourages collaborative learning among three learning ingredients, resulting in more generalizable representation across organs, diseases, and modalities; (2) outperforms fully supervised ImageNet models and increases robustness in small data regimes, reducing annotation cost across multiple medical imaging applications; (3) learns fine-grained semantic representation, facilitating accurate lesion localization with only image-level annotation; and (4) enhances state-of-the-art restorative approaches, revealing that DiRA is a general mechanism for united representation learning. All code and pretrained models are available at https://github.com/JLiangLab/DiRA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Haghighi_DiRA_Discriminative_Restorative_and_Adversarial_Learning_for_Self-Supervised_Medical_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Haghighi_DiRA_Discriminative_Restorative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Haghighi_DiRA_Discriminative_Restorative_and_Adversarial_Learning_for_Self-Supervised_Medical_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Haghighi_DiRA_Discriminative_Restorative_and_Adversarial_Learning_for_Self-Supervised_Medical_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Open Challenges in Deep Stereo: The Booster Dataset | Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano | We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ramirez_Open_Challenges_in_Deep_Stereo_The_Booster_Dataset_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ramirez_Open_Challenges_in_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ramirez_Open_Challenges_in_Deep_Stereo_The_Booster_Dataset_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ramirez_Open_Challenges_in_Deep_Stereo_The_Booster_Dataset_CVPR_2022_paper.html | CVPR 2022 | null |
Location-Free Human Pose Estimation | Xixia Xu, Yingguo Gao, Ke Yan, Xue Lin, Qi Zou | Human pose estimation (HPE) usually requires large-scale training data to reach high performance. However, it is rather time-consuming to collect high-quality and fine-grained annotations for human body. To alleviate this issue, we revisit HPE and propose a location-free framework without supervision of keypoint locations. We reformulate the regression-based HPE from the perspective of classification. Inspired by the CAM-based weakly-supervised object localization, we observe that the coarse keypoint locations can be acquired through the part-aware CAMs but unsatisfactory due to the gap between the fine-grained HPE and the object-level localization. To this end, we propose a customized transformer framework to mine the fine-grained representation of human context, equipped with the structural relation to capture subtle differences among keypoints. Concretely, we design a Multi-scale Spatial-guided Context Encoder to fully capture the global human context while focusing on the part-aware regions and a Relation-encoded Pose Prototype Generation module to encode the structural relations. All these works together for strengthening the weak supervision from image-level category labels on locations. Our model achieves competitive performance on three datasets when only supervised at a category-level and importantly, it can achieve comparable results with fully-supervised methods with only 25% location labels on MS-COCO and MPII. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Location-Free_Human_Pose_Estimation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.12619 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Location-Free_Human_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Location-Free_Human_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography | Jiaxiang Ren, Kicheon Park, Yingtian Pan, Haibin Ling | Optical coherence tomography angiography (OCTA) is an important imaging modality in many bioengineering tasks. The image quality of OCTA, however, is often degraded by Bulk Motion Artifacts (BMA), which are due to micromotion of subjects and typically appear as bright stripes surrounded by blurred areas. State-of-the-art methods usually treat BMA removal as a learning-based image inpainting problem, but require numerous training samples with nontrivial annotation. In addition, these methods discard the rich structural and appearance information carried in the BMA stripe region. To address these issues, in this paper we propose a self-supervised content-aware BMA removal model. First, the gradient-based structural information and appearance feature are extracted from the BMA area and injected into the model to capture more connectivity. Second, with easily collected defective masks, the model is trained in a self-supervised manner, in which only the clear areas are used for training while the BMA areas for inference. With the structural information and appearance feature from noisy image as references, our model can remove larger BMA and produce better visualizing result. In addition, only 2D images with defective masks are involved, hence improving the efficiency of our method. Experiments on OCTA of mouse cortex demonstrate that our model can remove most BMA with extremely large sizes and inconsistent intensities while previous methods fail. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Self-Supervised_Bulk_Motion_Artifact_Removal_in_Optical_Coherence_Tomography_Angiography_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2202.10360 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Self-Supervised_Bulk_Motion_Artifact_Removal_in_Optical_Coherence_Tomography_Angiography_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Self-Supervised_Bulk_Motion_Artifact_Removal_in_Optical_Coherence_Tomography_Angiography_CVPR_2022_paper.html | CVPR 2022 | null |
Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objects | Atsuhiro Noguchi, Umar Iqbal, Jonathan Tremblay, Tatsuya Harada, Orazio Gallo | Rendering articulated objects while controlling their poses is critical to applications such as virtual reality or animation for movies. Manipulating the pose of an object, however, requires the understanding of its underlying structure, that is, its joints and how they interact with each other. Unfortunately, assuming the structure to be known, as existing methods do, precludes the ability to work on new object categories. We propose to learn both the appearance and the structure of previously unseen articulated objects by observing them move from multiple views, with no joints annotation supervision, or information about the structure. We observe that 3D points that are static relative to one another should belong to the same part, and that adjacent parts that move relative to each other must be connected by a joint. To leverage this insight, we model the object parts in 3D as ellipsoids, which allows us to identify joints. We combine this explicit representation with an implicit one that compensates for the approximation introduced. We show that our method works for different structures, from quadrupeds, to single-arm robots, to humans. | https://openaccess.thecvf.com/content/CVPR2022/papers/Noguchi_Watch_It_Move_Unsupervised_Discovery_of_3D_Joints_for_Re-Posing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Noguchi_Watch_It_Move_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11347 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Noguchi_Watch_It_Move_Unsupervised_Discovery_of_3D_Joints_for_Re-Posing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Noguchi_Watch_It_Move_Unsupervised_Discovery_of_3D_Joints_for_Re-Posing_CVPR_2022_paper.html | CVPR 2022 | null |
PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking | Andreas Döring, Di Chen, Shanshan Zhang, Bernt Schiele, Jürgen Gall | Current research evaluates person search, multi-object tracking and multi-person pose estimation as separate tasks and on different datasets although these tasks are very akin to each other and comprise similar sub-tasks, e.g. person detection or appearance-based association of detected persons. Consequently, approaches on these respective tasks are eligible to complement each other. Therefore, we introduce PoseTrack21, a large-scale dataset for person search, multi-object tracking and multi-person pose tracking in real-world scenarios with a high diversity of poses. The dataset provides rich annotations like human pose annotations including annotations of joint occlusions, bounding box annotations even for small persons, and person-ids within and across video sequences. The dataset allows to evaluate multi-object tracking and multi-person pose tracking jointly with person re-identification or exploit structural knowledge of human poses to improve person search and tracking, particularly in the context of severe occlusions. With PoseTrack21, we want to encourage researchers to work on joint approaches that perform reasonably well on all three tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Doring_PoseTrack21_A_Dataset_for_Person_Search_Multi-Object_Tracking_and_Multi-Person_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Doring_PoseTrack21_A_Dataset_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Doring_PoseTrack21_A_Dataset_for_Person_Search_Multi-Object_Tracking_and_Multi-Person_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Doring_PoseTrack21_A_Dataset_for_Person_Search_Multi-Object_Tracking_and_Multi-Person_CVPR_2022_paper.html | CVPR 2022 | null |
Event-Based Video Reconstruction via Potential-Assisted Spiking Neural Network | Lin Zhu, Xiao Wang, Yi Chang, Jianing Li, Tiejun Huang, Yonghong Tian | Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports asynchronous, continuously per-pixel brightness changes called 'events' with high temporal resolution and high dynamic range. So far, the event-based image reconstruction methods are based on artificial neural networks (ANN) or hand-crafted spatiotemporal smoothing techniques. In this paper, we first implement the image reconstruction work via deep spiking neural network (SNN) architecture. As the bio-inspired neural networks, SNNs operating with asynchronous binary spikes distributed over time, can potentially lead to greater computational efficiency on event-driven hardware. We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron. We find that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks. Furthermore, to better utilize the temporal information, we propose a hybrid potential-assisted framework (PA-EVSNN) using the membrane potential of spiking neuron. The proposed neuron is referred as Adaptive Membrane Potential (AMP) neuron, which adaptively updates the membrane potential according to the input spikes. The experimental results demonstrate that our models achieve comparable performance to ANN-based models on IJRR, MVSEC, and HQF datasets. The energy consumptions of EVSNN and PA-EVSNN are 19.36 times and 7.75 times more computationally efficient than their ANN architectures, respectively. The code and pretrained model are available at https://sites.google.com/view/evsnn. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Event-Based_Video_Reconstruction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.10943 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Event-Based_Video_Reconstruction_via_Potential-Assisted_Spiking_Neural_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Maximal Coding Rate Reduction by Variational Forms | Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele | The principle of Maximal Coding Rate Reduction (MCR2) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high-dimensional data to allow for more robust training than standard approaches, such as cross-entropy minimization. However, despite the advantages that have been shown for MCR2 training, MCR2 suffers from a significant computational cost due to the need to evaluate and differentiate a significant number of log-determinant terms that grows linearly with the number of classes. By taking advantage of variational forms of spectral functions of a matrix, we reformulate the MCR2 objective to a form that can scale significantly without compromising training accuracy. Experiments in image classification demonstrate that our proposed formulation results in a significant speed up over optimizing the original MCR2 objective directly and often results in higher quality learned representations. Further, our approach may be of independent interest in other models that require computation of log-determinant forms, such as in system identification or normalizing flow models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Baek_Efficient_Maximal_Coding_Rate_Reduction_by_Variational_Forms_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Baek_Efficient_Maximal_Coding_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00077 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Baek_Efficient_Maximal_Coding_Rate_Reduction_by_Variational_Forms_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Baek_Efficient_Maximal_Coding_Rate_Reduction_by_Variational_Forms_CVPR_2022_paper.html | CVPR 2022 | null |
Ithaca365: Dataset and Driving Perception Under Repeated and Challenging Weather Conditions | Carlos A. Diaz-Ruiz, Youya Xia, Yurong You, Jose Nino, Junan Chen, Josephine Monica, Xiangyu Chen, Katie Luo, Yan Wang, Marc Emond, Wei-Lun Chao, Bharath Hariharan, Kilian Q. Weinberger, Mark Campbell | Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection process --- data is repeatedly recorded along a 15 km route under diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspondence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlusions and 2D/3D bounding boxes. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, continual learning, and anomaly detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Diaz-Ruiz_Ithaca365_Dataset_and_Driving_Perception_Under_Repeated_and_Challenging_Weather_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Diaz-Ruiz_Ithaca365_Dataset_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Diaz-Ruiz_Ithaca365_Dataset_and_Driving_Perception_Under_Repeated_and_Challenging_Weather_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Diaz-Ruiz_Ithaca365_Dataset_and_Driving_Perception_Under_Repeated_and_Challenging_Weather_CVPR_2022_paper.html | CVPR 2022 | null |
AutoLoss-GMS: Searching Generalized Margin-Based Softmax Loss Function for Person Re-Identification | Hongyang Gu, Jianmin Li, Guangyuan Fu, Chifong Wong, Xinghao Chen, Jun Zhu | Person re-identification is a hot topic in computer vision, and the loss function plays a vital role in improving the discrimination of the learned features. However, most existing models utilize the hand-crafted loss functions, which are usually sub-optimal and challenging to be designed. In this paper, we propose a novel method, AutoLoss-GMS, to search the better loss function in the space of generalized margin-based softmax loss function for person re-identification automatically. Specifically, the generalized margin-based softmax loss function is first decomposed into two computational graphs and a constant. Then a general searching framework built upon the evolutionary algorithm is proposed to search for the loss function efficiently. The computational graph is constructed with a forward method, which can construct much richer loss function forms than the backward method used in existing works. In addition to the basic in-graph mutation operations, the cross-graph mutation operation is designed to further improve the offspring's diversity. The loss-rejection protocol, equivalence-check strategy and the predictor-based promising-loss chooser are developed to improve the search efficiency. Finally, experimental results demonstrate that the searched loss functions can achieve state-of-the-art performance and be transferable across different models and datasets in person re-identification. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_AutoLoss-GMS_Searching_Generalized_Margin-Based_Softmax_Loss_Function_for_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gu_AutoLoss-GMS_Searching_Generalized_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_AutoLoss-GMS_Searching_Generalized_Margin-Based_Softmax_Loss_Function_for_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_AutoLoss-GMS_Searching_Generalized_Margin-Based_Softmax_Loss_Function_for_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
YouMVOS: An Actor-Centric Multi-Shot Video Object Segmentation Dataset | Donglai Wei, Siddhant Kharbanda, Sarthak Arora, Roshan Roy, Nishant Jain, Akash Palrecha, Tanav Shah, Shray Mathur, Ritik Mathur, Abhijay Kemkar, Anirudh Chakravarthy, Zudi Lin, Won-Dong Jang, Yansong Tang, Song Bai, James Tompkin, Philip H.S. Torr, Hanspeter Pfister | Many video understanding tasks require analyzing multi-shot videos, but existing datasets for video object segmentation (VOS) only consider single-shot videos. To address this challenge, we collected a new dataset---YouMVOS---of 200 popular YouTube videos spanning ten genres, where each video is on average five minutes long and with 75 shots. We selected recurring actors and annotated 431K segmentation masks at a frame rate of six, exceeding previous datasets in average video duration, object variation, and narrative structure complexity. We incorporated good practices of model architecture design, memory management, and multi-shot tracking into an existing video segmentation method to build competitive baseline methods. Through error analysis, we found that these baselines still fail to cope with cross-shot appearance variation on our YouMVOS dataset. Thus, our dataset poses new challenges in multi-shot segmentation towards better video analysis. Data, code, and pre-trained models are available at https://donglaiw.github.io/proj/youMVOS | https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_YouMVOS_An_Actor-Centric_Multi-Shot_Video_Object_Segmentation_Dataset_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_YouMVOS_An_Actor-Centric_Multi-Shot_Video_Object_Segmentation_Dataset_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_YouMVOS_An_Actor-Centric_Multi-Shot_Video_Object_Segmentation_Dataset_CVPR_2022_paper.html | CVPR 2022 | null |
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation | Lukas Hoyer, Dengxin Dai, Luc Van Gool | As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large number of methods propose new adaptation strategies, they are mostly based on outdated network architectures. As the influence of recent network architectures has not been systematically studied, we first benchmark different network architectures for UDA and newly reveal the potential of Transformers for UDA semantic segmentation. Based on the findings, we propose a novel UDA method, DAFormer. The network architecture of DAFormer consists of a Transformer encoder and a multi-level context-aware feature fusion decoder. It is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting to the source domain: While (1) Rare Class Sampling on the source domain improves the quality of the pseudo-labels by mitigating the confirmation bias of self-training toward common classes, (2) a Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote feature transfer from ImageNet pretraining. DAFormer represents a major advance in UDA. It improves the state of the art by 10.8 mIoU for GTA-to-Cityscapes and 5.4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes such as train, bus, and truck well. The implementation is available at https://github.com/lhoyer/DAFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hoyer_DAFormer_Improving_Network_Architectures_and_Training_Strategies_for_Domain-Adaptive_Semantic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hoyer_DAFormer_Improving_Network_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hoyer_DAFormer_Improving_Network_Architectures_and_Training_Strategies_for_Domain-Adaptive_Semantic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hoyer_DAFormer_Improving_Network_Architectures_and_Training_Strategies_for_Domain-Adaptive_Semantic_CVPR_2022_paper.html | CVPR 2022 | null |
Sound-Guided Semantic Image Manipulation | Seung Hyun Lee, Wonseok Roh, Wonmin Byeon, Sang Ho Yoon, Chanyoung Kim, Jinkyu Kim, Sangpil Kim | The recent success of the generative model shows that leveraging the multi-modal embedding space can manipulate an image using text information. However, manipulating an image with other sources rather than text, such as sound, is not easy due to the dynamic characteristics of the sources. Especially, sound can convey vivid emotions and dynamic expressions of the real world. Here, we propose a framework that directly encodes sound into the multi-modal (image-text) embedding space and manipulates an image from the space. Our audio encoder is trained to produce a latent representation from an audio input, which is forced to be aligned with image and text representations in the multi-modal embedding space. We use a direct latent optimization method based on aligned embeddings for sound-guided image manipulation. We also show that our method can mix different modalities, i.e., text and audio, which enrich the variety of the image modification. The experiments on zero-shot audio classification and semantic-level image classification show that our proposed model outperforms other text and sound-guided state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Sound-Guided_Semantic_Image_Manipulation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Sound-Guided_Semantic_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00007 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Sound-Guided_Semantic_Image_Manipulation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Sound-Guided_Semantic_Image_Manipulation_CVPR_2022_paper.html | CVPR 2022 | null |
Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification | Jiangtao Xie, Fei Long, Jiaming Lv, Qilong Wang, Peihua Li | Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://www.peihuali.org/DeepBDC. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Joint_Distribution_Matters_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04567 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Joint_Distribution_Matters_Deep_Brownian_Distance_Covariance_for_Few-Shot_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
Proper Reuse of Image Classification Features Improves Object Detection | Cristina Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin | A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task. In object detection specifically,the feature backbone is typically initialized with ImageNet classifier weights and fine-tuned on the object detection task. Recent works show this is not strictly necessary under longer training regimes and provide recipes for training the backbone from scratch. We investigate the opposite direction of this end-to-end training trend: we show that an extreme form of knowledge preservation--freezing the classifier-initialized backbone-- consistently improves many different detection models, and leads to considerable resource savings. We hypothesize and corroborate experimentally that the remaining detector components capacity and structure is a crucial factor in leveraging the frozen backbone. Immediate applications of our findings include performance improvements on hard cases like detection of long-tail object classes and computational and memory re-source savings that contribute to making the field more accessible to researchers with access to fewer computational resources. | https://openaccess.thecvf.com/content/CVPR2022/papers/Vasconcelos_Proper_Reuse_of_Image_Classification_Features_Improves_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Vasconcelos_Proper_Reuse_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vasconcelos_Proper_Reuse_of_Image_Classification_Features_Improves_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vasconcelos_Proper_Reuse_of_Image_Classification_Features_Improves_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
MetaPose: Fast 3D Pose From Multiple Views Without 3D Supervision | Ben Usman, Andrea Tagliasacchi, Kate Saenko, Avneesh Sud | In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perform this task with high precision and minimal latency overhead. The proposed model takes into account joint location uncertainty due to occlusion from multiple views, and requires only 2D keypoint data for training. Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines on the well-established Human3.6M dataset, as well as the more challenging in-the-wild Ski-Pose PTZ dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Usman_MetaPose_Fast_3D_Pose_From_Multiple_Views_Without_3D_Supervision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Usman_MetaPose_Fast_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2108.04869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Usman_MetaPose_Fast_3D_Pose_From_Multiple_Views_Without_3D_Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Usman_MetaPose_Fast_3D_Pose_From_Multiple_Views_Without_3D_Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Human-Gaze-Target Detection With Transformers | Danyang Tu, Xiongkuo Min, Huiyu Duan, Guodong Guo, Guangtao Zhai, Wei Shen | In this paper, we propose an effective and efficient method for Human-Gaze-Target (HGT) detection, i.e., gaze following. Current approaches decouple the HGT detection task into separate branches of salient object detection and human gaze prediction, employing a two-stage framework where human head locations must first be detected and then be fed into the next gaze target prediction sub-network. In contrast, we redefine the HGT detection task as detecting human head locations and their gaze targets, simultaneously. By this way, our method, named Human-Gaze-Target detection TRansformer or HGTTR, streamlines the HGT detection pipeline by eliminating all other additional components. HGTTR reasons about the relations of salient objects and human gaze from the global image context. Moreover, unlike existing two-stage methods that require human head locations as input and can predict only one human's gaze target at a time, HGTTR can directly predict the locations of all people and their gaze targets at one time in an end-to-end manner. The effectiveness and robustness of our proposed method are verified with extensive experiments on the two standard benchmark datasets, GazeFollowing and VideoAttentionTarget. Without bells and whistles, HGTTR outperforms existing state-of-the-art methods by large margins (6.4 mAP gain on GazeFollowing and 10.3 mAP gain on VideoAttentionTarget) with a much simpler architecture. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tu_End-to-End_Human-Gaze-Target_Detection_With_Transformers_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.10433 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tu_End-to-End_Human-Gaze-Target_Detection_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tu_End-to-End_Human-Gaze-Target_Detection_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
The Devil Is in the Pose: Ambiguity-Free 3D Rotation-Invariant Learning via Pose-Aware Convolution | Ronghan Chen, Yang Cong | Recent progress in introducing rotation invariance (RI) to 3D deep learning methods is mainly made by designing RI features to replace 3D coordinates as input. The key to this strategy lies in how to restore the global information that is lost by the input RI features. Most state-of-the-arts achieve this by incurring additional blocks or complex global representations, which is time-consuming and ineffective. In this paper, we real that the global information loss stems from an unexplored pose information loss problem, i.e., common convolution layers cannot capture the relative poses between RI features, thus hindering the global information to be hierarchically aggregated in the deep networks. To address this problem, we develop a Pose-aware Rotation Invariant Convolution (i.e., PaRI-Conv), which dynamically adapts its kernels based on the relative poses. Specifically, in each PaRI-Conv layer, a lightweight Augmented Point Pair Feature (APPF) is designed to fully encode the RI relative pose information. Then, we propose to synthesize a factorized dynamic kernel, which reduces the computational cost and memory burden by decomposing it into a shared basis matrix and a pose-aware diagonal matrix that can be learned from the APPF. Extensive experiments on shape classification and part segmentation tasks show that our PaRI-Conv surpasses the state-of-the-art RI methods while being more compact and efficient. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_The_Devil_Is_in_the_Pose_Ambiguity-Free_3D_Rotation-Invariant_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_The_Devil_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.15210 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_The_Devil_Is_in_the_Pose_Ambiguity-Free_3D_Rotation-Invariant_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_The_Devil_Is_in_the_Pose_Ambiguity-Free_3D_Rotation-Invariant_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Compositional Temporal Grounding With Structured Variational Cross-Graph Correspondence Learning | Juncheng Li, Junlin Xie, Long Qian, Linchao Zhu, Siliang Tang, Fei Wu, Yi Yang, Yueting Zhuang, Xin Eric Wang | Temporal grounding in videos aims to localize one target video segment that semantically corresponds to a given query sentence. Thanks to the semantic diversity of natural language descriptions, temporal grounding allows activity grounding beyond pre-defined classes and has received increasing attention in recent years. The semantic diversity is rooted in the principle of compositionality in linguistics, where novel semantics can be systematically described by combining known words in novel ways (compositional generalization). However, current temporal grounding datasets do not specifically test for the compositional generalizability. To systematically measure the compositional generalizability of temporal grounding models, we introduce a new Compositional Temporal Grounding task and construct two new dataset splits, i.e., Charades-CG and ActivityNet-CG. Evaluating the state-of-the-art methods on our new dataset splits, we empirically find that they fail to generalize to queries with novel combinations of seen words. To tackle this challenge, we propose a variational cross-graph reasoning framework that explicitly decomposes video and language into multiple structured hierarchies and learns fine-grained semantic correspondence among them. Experiments illustrate the superior compositional generalizability of our approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Compositional_Temporal_Grounding_With_Structured_Variational_Cross-Graph_Correspondence_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Compositional_Temporal_Grounding_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Compositional_Temporal_Grounding_With_Structured_Variational_Cross-Graph_Correspondence_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Compositional_Temporal_Grounding_With_Structured_Variational_Cross-Graph_Correspondence_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline | Pengyu Zhang, Jie Zhao, Dong Wang, Huchuan Lu, Xiang Ruan | With the popularity of multi-modal sensors, visible-thermal (RGB-T) object tracking is to achieve robust performance and wider application scenarios with the guidance of objects' temperature information. However, the lack of paired training samples is the main bottleneck for unlocking the power of RGB-T tracking. Since it is laborious to collect high-quality RGB-T sequences, recent benchmarks only provide test sequences. In this paper, we construct a large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV), including 500 sequences with 1.7 million high-resolution (1920*1080 pixels) frame pairs. In addition, comprehensive applications (short-term tracking, long-term tracking and segmentation mask prediction) with diverse categories and scenes are considered for exhaustive evaluation. Moreover, we provide a coarse-to-fine attribute annotation, where frame-level attributes are provided to exploit the potential of challenge-specific trackers. In addition, we design a new RGB-T baseline, named Hierarchical Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels. Numerous experiments on several datasets are conducted to reveal the effectiveness of HMFT and the complement of different fusion types. The project is available at here. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Visible-Thermal_UAV_Tracking_A_Large-Scale_Benchmark_and_New_Baseline_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Visible-Thermal_UAV_Tracking_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04120 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Visible-Thermal_UAV_Tracking_A_Large-Scale_Benchmark_and_New_Baseline_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Visible-Thermal_UAV_Tracking_A_Large-Scale_Benchmark_and_New_Baseline_CVPR_2022_paper.html | CVPR 2022 | null |
Future Transformer for Long-Term Action Anticipation | Dayoung Gong, Joonseok Lee, Manjin Kim, Seong Jong Ha, Minsu Cho | The task of predicting future actions from a video is crucial for a real-world agent interacting with others. When anticipating actions in the distant future, we humans typically consider long-term relations over the whole sequence of actions, i.e., not only observed actions in the past but also potential actions in the future. In a similar spirit, we propose an end-to-end attention model for action anticipation, dubbed Future Transformer (FUTR), that leverages global attention over all input frames and output tokens to predict a minutes-long sequence of future actions. Unlike the previous autoregressive models, the proposed method learns to predict the whole sequence of future actions in parallel decoding, enabling more accurate and fast inference for long-term anticipation. We evaluate our methods on two standard benchmarks for long-term action anticipation, Breakfast and 50 Salads, achieving state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gong_Future_Transformer_for_Long-Term_Action_Anticipation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gong_Future_Transformer_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.14022 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_Future_Transformer_for_Long-Term_Action_Anticipation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_Future_Transformer_for_Long-Term_Action_Anticipation_CVPR_2022_paper.html | CVPR 2022 | null |
Optimal LED Spectral Multiplexing for NIR2RGB Translation | Lei Liu, Yuze Chen, Junchi Yan, Yinqiang Zheng | The industry practice for night video surveillance is to use auxiliary near-infrared (NIR) LED diodes, usually centered at 850nm or 940nm, for scene illumination. NIR LED diodes are used to save power consumption while hiding the surveillance coverage area from naked human eyes. The captured images are almost monochromatic, and visual color and texture tend to disappear, which hinders human and machine perception. A few existing studies have tried to convert such NIR images to RGB images through deep learning, which can not provide satisfying results, nor generalize well beyond the training dataset. In this paper, we aim to break the fundamental restrictions on reliable NIR-to-RGB (NIR2RGB) translation by examining the imaging mechanism of single-chip silicon-based RGB cameras under NIR illuminations, and propose to retrieve the optimal LED multiplexing via deep learning. Experimental results show that this translation task can be significantly improved by properly multiplexing NIR LEDs close to the visible spectral range than using 850nm and 940nm LEDs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Optimal_LED_Spectral_Multiplexing_for_NIR2RGB_Translation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Optimal_LED_Spectral_Multiplexing_for_NIR2RGB_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Optimal_LED_Spectral_Multiplexing_for_NIR2RGB_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Spatial Invariance of Convolutional Networks for Object Counting | Zhi-Qi Cheng, Qi Dai, Hong Li, Jingkuan Song, Xiao Wu, Alexander G. Hauptmann | Previous work generally believes that improving the spatial invariance of convolutional networks is the key to object counting. However, after verifying several mainstream counting networks, we surprisingly found too strict pixel-level spatial invariance would cause overfit noise in the density map generation. In this paper, we try to use locally connected Gaussian kernels to replace the original convolution filter to estimate the spatial position in the density map. The purpose of this is to allow the feature extraction process to potentially stimulate the density map generation process to overcome the annotation noise. Inspired by previous work, we propose a low-rank approximation accompanied with translation invariance to favorably implement the approximation of massive Gaussian convolution. Our work points a new direction for follow-up research, which should investigate how to properly relax the overly strict pixel-level spatial invariance for object counting. We evaluate our methods on 4 mainstream object counting networks (i.e., MCNN, CSRNet, SANet, and ResNet-50). Extensive experiments were conducted on 7 popular benchmarks for 3 applications (i.e., crowd, vehicle, and plant counting). Experimental results show that our methods significantly outperform other state-of-the-art methods and achieve promising learning of the spatial position of objects. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Rethinking_Spatial_Invariance_of_Convolutional_Networks_for_Object_Counting_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Rethinking_Spatial_Invariance_of_Convolutional_Networks_for_Object_Counting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Rethinking_Spatial_Invariance_of_Convolutional_Networks_for_Object_Counting_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Video Transformer | Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, Michael S. Ryoo | In this paper, we propose self-supervised training for video transformers using unlabeled video data. From a given video, we create local and global spatiotemporal views with varying spatial sizes and frame rates. Our self-supervised objective seeks to match the features of these different views representing the same video, to be invariant to spatiotemporal variations in actions. To the best of our knowledge, the proposed approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT). Further, owing to the flexibility of Transformer models, SVT supports slow-fast video processing within a single architecture using dynamically adjusted positional encoding and supports long-term relationship modeling along spatiotemporal dimensions. Our approach performs well on four action recognition benchmarks (Kinetics-400, UCF-101, HMDB-51, and SSv2) and converges faster with small batch sizes. Code is available at: https://git.io/J1juJ | https://openaccess.thecvf.com/content/CVPR2022/papers/Ranasinghe_Self-Supervised_Video_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ranasinghe_Self-Supervised_Video_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01514 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ranasinghe_Self-Supervised_Video_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ranasinghe_Self-Supervised_Video_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
AutoRF: Learning 3D Object Radiance Fields From Single View Observations | Norman Müller, Andrea Simonelli, Lorenzo Porzi, Samuel Rota Bulò, Matthias Nießner, Peter Kontschieder | We introduce AutoRF - a new approach for learning neural 3D object representations where each object in the training set is observed by only a single view. This setting is in stark contrast to the majority of existing works that leverage multiple views of the same object, employ explicit priors during training, or require pixel-perfect annotations. To address this challenging setting, we propose to learn a normalized, object-centric representation whose embedding describes and disentangles shape, appearance, and pose. Each encoding provides well-generalizable, compact information about the object of interest, which is decoded in a single-shot into a new target view, thus enabling novel view synthesis. We further improve the reconstruction quality by optimizing shape and appearance codes at test time by fitting the representation tightly to the input image. In a series of experiments, we show that our method generalizes well to unseen objects, even across different datasets of challenging real-world street scenes such as nuScenes, KITTI, and Mapillary Metropolis. Additional results can be found on our project page https://sirwyver.github.io/AutoRF/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Muller_AutoRF_Learning_3D_Object_Radiance_Fields_From_Single_View_Observations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Muller_AutoRF_Learning_3D_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Muller_AutoRF_Learning_3D_Object_Radiance_Fields_From_Single_View_Observations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Muller_AutoRF_Learning_3D_Object_Radiance_Fields_From_Single_View_Observations_CVPR_2022_paper.html | CVPR 2022 | null |
Expanding Large Pre-Trained Unimodal Models With Multimodal Information Injection for Image-Text Multimodal Classification | Tao Liang, Guosheng Lin, Mingyang Wan, Tianrui Li, Guojun Ma, Fengmao Lv | Fine-tuning pre-trained models for downstream tasks is mainstream in deep learning. However, the pre-trained models are limited to be fine-tuned by data from a specific modality. For example, as a visual model, DenseNet cannot directly take the textual data as its input. Hence, although the large pre-trained models such as DenseNet or BERT have a great potential for the downstream recognition tasks, they have weaknesses in leveraging multimodal information, which is a new trend of deep learning. This work focuses on fine-tuning pre-trained unimodal models with multimodal inputs of image-text pairs and expanding them for image-text multimodal recognition. To this end, we propose the Multimodal Information Injection Plug-in (MI2P) which is attached to different layers of the unimodal models (e.g., DenseNet and BERT). The proposed MI2P unit provides the path to integrate the information of other modalities into the unimodal models. Specifically, MI2P performs cross-modal feature transformation by learning the fine-grained correlations between the visual and textual features. Through the proposed MI2P unit, we can inject the language information into the vision backbone by attending the word-wise textual features to different visual channels, as well as inject the visual information into the language backbone by attending the channel-wise visual features to different textual words. Armed with the MI2P attachments, the pre-trained unimodal models can be expanded to process multimodal data without the need to change the network structures. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Expanding_Large_Pre-Trained_Unimodal_Models_With_Multimodal_Information_Injection_for_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Expanding_Large_Pre-Trained_Unimodal_Models_With_Multimodal_Information_Injection_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Expanding_Large_Pre-Trained_Unimodal_Models_With_Multimodal_Information_Injection_for_CVPR_2022_paper.html | CVPR 2022 | null |
Neural RGB-D Surface Reconstruction | Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, Justus Thies | Obtaining high-quality 3D reconstructions of room-scale scenes is of paramount importance for upcoming applications in AR or VR. These range from mixed reality applications for teleconferencing, virtual measuring, virtual room planing, to robotic applications. While current volume-based view synthesis methods that use neural radiance fields (NeRFs) show promising results in reproducing the appearance of an object or scene, they do not reconstruct an actual surface. The volumetric representation of the surface based on densities leads to artifacts when a surface is extracted using Marching Cubes, since during optimization, densities are accumulated along the ray and are not used at a single sample point in isolation. Instead of this volumetric representation of the surface, we propose to represent the surface using an implicit function (truncated signed distance function). We show how to incorporate this representation in the NeRF framework, and extend it to use depth measurements from a commodity RGB-D sensor, such as a Kinect. In addition, we propose a pose and camera refinement technique which improves the overall reconstruction quality. In contrast to concurrent work on integrating depth priors in NeRF which concentrates on novel view synthesis, our approach is able to reconstruct high-quality, metrical 3D reconstructions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Azinovic_Neural_RGB-D_Surface_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Azinovic_Neural_RGB-D_Surface_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Azinovic_Neural_RGB-D_Surface_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Azinovic_Neural_RGB-D_Surface_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
ClusterGNN: Cluster-Based Coarse-To-Fine Graph Neural Network for Efficient Feature Matching | Yan Shi, Jun-Xiong Cai, Yoli Shavit, Tai-Jiang Mu, Wensen Feng, Kai Zhang | Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features. Motivated by a prior observation that self- and cross- attention matrices converge to a sparse representation, we propose ClusterGNN, an attentional GNN architecture which operates on clusters for learning the feature matching task. Using a progressive clustering module we adaptively divide keypoints into different subgraphs to reduce redundant connectivity, and employ a coarse-to-fine paradigm for mitigating miss-classification within images. Our approach yields a 59.7% reduction in runtime and 58.4% reduction in memory consumption for dense detection, compared to current state-of-the-art GNN-based matching, while achieving a competitive performance on various computer vision tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_ClusterGNN_Cluster-Based_Coarse-To-Fine_Graph_Neural_Network_for_Efficient_Feature_Matching_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.11700 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_ClusterGNN_Cluster-Based_Coarse-To-Fine_Graph_Neural_Network_for_Efficient_Feature_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_ClusterGNN_Cluster-Based_Coarse-To-Fine_Graph_Neural_Network_for_Efficient_Feature_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation | Mohsen Gholami, Bastian Wandt, Helge Rhodin, Rabab Ward, Z. Jane Wang | This paper addresses the problem of cross-dataset generalization of 3D human pose estimation models. Testing a pre-trained 3D pose estimator on a new dataset results in a major performance drop. Previous methods have mainly addressed this problem by improving the diversity of the training data. We argue that diversity alone is not sufficient and that the characteristics of the training data need to be adapted to those of the new dataset such as camera viewpoint, position, human actions, and body size. To this end, we propose AdaptPose, an end-to-end framework that generates synthetic 3D human motions from a source dataset and uses them to fine-tune a 3D pose estimator. AdaptPose follows an adversarial training scheme. From a source 3D pose the generator generates a sequence of 3D poses and a camera orientation that is used to project the generated poses to a novel view. Without any 3D labels or camera information AdaptPose successfully learns to create synthetic 3D poses from the target dataset while only being trained on 2D poses. In experiments on the Human3.6M, MPI-INF-3DHP, 3DPW, and Ski-Pose datasets our method outperforms previous work in cross-dataset evaluations by 14% and previous semi-supervised learning methods that use partial 3D annotations by 16%. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gholami_AdaptPose_Cross-Dataset_Adaptation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gholami_AdaptPose_Cross-Dataset_Adaptation_for_3D_Human_Pose_Estimation_by_Learnable_CVPR_2022_paper.html | CVPR 2022 | null |
ClothFormer: Taming Video Virtual Try-On in All Module | Jianbin Jiang, Tan Wang, He Yan, Junhui Liu | The task of video virtual try-on aims to fit the target clothes to a person in the video with spatio-temporal consistency. Despite tremendous progress of image virtual try-on, they lead to inconsistency between frames when applied to videos. Limited work also explored the task of video-based virtual try-on but failed to produce visually pleasing and temporally coherent results. Moreover, there are two other key challenges: 1) how to generate accurate warping when occlusions appear in the clothing region; 2) how to generate clothes and non-target body parts (e.g. arms, neck) in harmony with the complicated background; To address them, we propose a novel video virtual try-on framework, ClothFormer, which successfully synthesizes realistic, harmonious, and spatio-temporal consistent results in complicated environment. In particular, ClothFormer involves three major modules. First, a two-stage anti-occlusion warping module that predicts an accurate dense flow mapping between the body regions and the clothing regions. Second, an appearance-flow tracking module utilizes ridge regression and optical flow correction to smooth the dense flow sequence and generate a temporally smooth warped clothing sequence. Third, a dual-stream transformer extracts and fuses clothing textures, person features, and environment information to generate realistic try-on videos. Through rigorous experiments, we demonstrate that our method highly surpasses the baselines in terms of synthesized video quality both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_ClothFormer_Taming_Video_Virtual_Try-On_in_All_Module_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_ClothFormer_Taming_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12151 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_ClothFormer_Taming_Video_Virtual_Try-On_in_All_Module_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_ClothFormer_Taming_Video_Virtual_Try-On_in_All_Module_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Domain Adaptive Teacher for Object Detection | Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, Peter Vajda | We address the task of domain adaptation in object detection, where there is a domain gap between a domain with annotations (source) and a domain of interest without annotations (target). As an effective semi-supervised learning method, the teacher-student framework (a student model is supervised by the pseudo labels from a teacher model) has also yielded a large accuracy gain in cross-domain object detection. However, it suffers from the domain shift and generates many low-quality pseudo labels (e.g., false positives), which leads to sub-optimal performance. To mitigate this problem, we propose a teacher-student framework named Adaptive Teacher (AT) which leverages domain adversarial learning and weak-strong data augmentation to address the domain gap. Specifically, we employ feature-level adversarial training in the student model, allowing features derived from the source and target domains to share similar distributions. This process ensures the student model produces domain-invariant features. Furthermore, we apply weak-strong augmentation and mutual learning between the teacher model (taking data from the target domain) and the student model (taking data from both domains). This enables the teacher model to learn the knowledge from the student model without being biased to the source domain. We show that AT demonstrates superiority over existing approaches and even Oracle (fully-supervised) models by a large margin. For example, we achieve 50.9% (49.3%) mAP on Foggy Cityscape (Clipart1K), which is 9.2% (5.2%) and 8.2% (11.0%) higher than previous state-of-the-art and Oracle, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Cross-Domain_Adaptive_Teacher_for_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.13216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Domain_Adaptive_Teacher_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Domain_Adaptive_Teacher_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
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