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Discrete Time Convolution for Fast Event-Based Stereo | Kaixuan Zhang, Kaiwei Che, Jianguo Zhang, Jie Cheng, Ziyang Zhang, Qinghai Guo, Luziwei Leng | Inspired by biological retina, dynamical vision sensor transmits events of instantaneous changes of pixel intensity, giving it a series of advantages over traditional frame-based camera, such as high dynamical range, high temporal resolution and low power consumption. However, extracting information from highly asynchronous event data is a challenging task. Inspired by continuous dynamics of biological neuron models, we propose a novel encoding method for sparse events - continuous time convolution (CTC) - which learns to model the spatial feature of the data with intrinsic dynamics. Adopting channel-wise parameterization, temporal dynamics of the model is synchronized on the same feature map and diverges across different ones, enabling it to embed data in a variety of temporal scales. Abstracted from CTC, we further develop discrete time convolution (DTC) which accelerates the process with lower computational cost. We apply these methods to event-based multi-view stereo matching where they surpass state-of-the-art methods on benchmark criteria of the MVSEC dataset. Spatially sparse event data often leads to inaccurate estimation of edges and local contours. To address this problem, we propose a dual-path architecture in which the feature map is complemented by underlying edge information from original events extracted with spatially-adaptive denormalization. We demonstrate the superiority of our model in terms of speed (up to 110 FPS), accuracy and robustness, showing a great potential for real-time fast depth estimation. Finally, we perform experiments on the recent DSEC dataset to demonstrate the general usage of our model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Discrete_Time_Convolution_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Discrete_Time_Convolution_for_Fast_Event-Based_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
An Image Patch Is a Wave: Phase-Aware Vision MLP | Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Yanxi Li, Chao Xu, Yunhe Wang | In the field of computer vision, recent works show that a pure MLP architecture mainly stacked by fully-connected layers can achieve competing performance with CNN and transformer. An input image of vision MLP is usually split into multiple tokens (patches), while the existing MLP models directly aggregate them with fixed weights, neglecting the varying semantic information of tokens from different images. To dynamically aggregate tokens, we propose to represent each token as a wave function with two parts, amplitude and phase. Amplitude is the original feature and the phase term is a complex value changing according to the semantic contents of input images. Introducing the phase term can dynamically modulate the relationship between tokens and fixed weights in MLP. Based on the wave-like token representation, we establish a novel Wave-MLP architecture for vision tasks. Extensive experiments demonstrate that the proposed Wave-MLP is superior to the state-of-the-art MLP architectures on various vision tasks such as image classification, object detection and semantic segmentation. The source code is available at https://github.com/huawei-noah/CV-Backbones/tree/master/wavemlp_pytorch and https://gitee.com/mindspore/models/tree/master/research/cv/wave_mlp. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_An_Image_Patch_Is_a_Wave_Phase-Aware_Vision_MLP_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_An_Image_Patch_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12294 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_An_Image_Patch_Is_a_Wave_Phase-Aware_Vision_MLP_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_An_Image_Patch_Is_a_Wave_Phase-Aware_Vision_MLP_CVPR_2022_paper.html | CVPR 2022 | null |
Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination | Yiqun Mei, Pengfei Guo, Vishal M. Patel | In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult problem. Recent methods attempting to fill the gap via synthesis have achieved promising results, but their performance is still limited by the scarcity of paired training data. In practice, large-scale heterogeneous face data are often inaccessible due to the high cost of acquisition and annotation process as well as privacy regulations. In this paper, we propose a new face hallucination paradigm for HFR, which not only enables data-efficient synthesis but also allows to scale up model training without breaking any privacy policy. Unlike existing methods that learn face synthesis entirely from scratch, our approach is particularly designed to take advantage of rich and diverse facial priors from visible domain for more faithful hallucination. On the other hand, large-scale training is enabled by introducing a new federated learning scheme to allow institution-wise collaborations while avoiding explicit data sharing. Extensive experiments demonstrate the advantages of our approach in tackling HFR under current data limitations. In a unified framework, our method yields the state-of-the-art hallucination results on multiple HFR datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mei_Escaping_Data_Scarcity_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16669 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.html | CVPR 2022 | null |
Visual Acoustic Matching | Changan Chen, Ruohan Gao, Paul Calamia, Kristen Grauman | We introduce the visual acoustic matching task, in which an audio clip is transformed to sound like it was recorded in a target environment. Given an image of the target environment and a waveform for the source audio, the goal is to re-synthesize the audio to match the target room acoustics as suggested by its visible geometry and materials. To address this novel task, we propose a cross-modal transformer model that uses audio-visual attention to inject visual properties into the audio and generate realistic audio output. In addition, we devise a self-supervised training objective that can learn acoustic matching from in-the-wild Web videos, despite their lack of acoustically mismatched audio. We demonstrate that our approach successfully translates human speech to a variety of real-world environments depicted in images, outperforming both traditional acoustic matching and more heavily supervised baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Visual_Acoustic_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Visual_Acoustic_Matching_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2202.06875 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Visual_Acoustic_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Visual_Acoustic_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Shunted Self-Attention via Multi-Scale Token Aggregation | Sucheng Ren, Daquan Zhou, Shengfeng He, Jiashi Feng, Xinchao Wang | Recent Vision Transformer (ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to its competence in modeling long-range dependencies of image patches or tokens via self-attention. These models, however, usually designate the similar receptive fields of each token feature within each layer. Such a constraint inevitably limits the ability of each self-attention layer in capturing multi-scale features, thereby leading to performance degradation in handling images with multiple objects of different scales. To address this issue, we propose a novel and generic strategy, termed shunted self-attention (SSA), that allows ViTs to model the attentions at hybrid scales per attention layer. The key idea of SSA is to inject heterogeneous receptive field sizes into tokens: before computing the self-attention matrix, it selectively merges tokens to represent larger object features while keeping certain tokens to preserve fine-grained features. This novel merging scheme enables the self-attention to learn relationships between objects with different sizes and simultaneously reduces the token numbers and the computational cost. Extensive experiments across various tasks demonstrate the superiority of SSA. Specifically, the SSA-based transformer achieves 84.0% Top-1 accuracy and outperforms the state-of-the-art Focal Transformer on ImageNet with only half of the model size and computation cost, and surpasses Focal Transformer by 1.3 mAP on COCO and 2.9 mIOU on ADE20K under similar parameter and computation cost. Code has been released at \href https://github.com/OliverRensu/Shunted-Transformer https://github.com/OliverRensu/Shunted-Transformer . | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Shunted_Self-Attention_via_Multi-Scale_Token_Aggregation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.15193 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Shunted_Self-Attention_via_Multi-Scale_Token_Aggregation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Shunted_Self-Attention_via_Multi-Scale_Token_Aggregation_CVPR_2022_paper.html | CVPR 2022 | null |
Shadows Can Be Dangerous: Stealthy and Effective Physical-World Adversarial Attack by Natural Phenomenon | Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji | Estimating the risk level of adversarial examples is essential for safely deploying machine learning models in the real world. One popular approach for physical-world attacks is to adopt the "sticker-pasting" strategy, which however suffers from some limitations, including difficulties in access to the target or printing by valid color. A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector. However, the added optical patterns are artificial but not natural. Thus, they are still conspicuous and attention-grabbed, and can be easily noticed by humans. In this paper, we study a new type of optical adversarial examples, in which the perturbations are generated by a very common natural phenomenon, shadow, to achieve naturalistic and stealthy physical-world adversarial attack under the black-box setting. We extensively evaluate the effectiveness of this new attack on both simulated and real-world environments. Experimental results on traffic sign recognition demonstrate that our algorithm can generate adversarial examples effectively, reaching 98.23% and 90.47% success rates on LISA and GTSRB test sets respectively, while continuously misleading a moving camera over 95% of the time in real-world scenarios. We also offer discussions about the limitations and the defense mechanism of this attack. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.03818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_Shadows_Can_Be_Dangerous_Stealthy_and_Effective_Physical-World_Adversarial_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
ImplicitAtlas: Learning Deformable Shape Templates in Medical Imaging | Jiancheng Yang, Udaranga Wickramasinghe, Bingbing Ni, Pascal Fua | Deep implicit shape models have become popular in the computer vision community at large but less so for biomedical applications. This is in part because large training databases do not exist and in part because biomedical annotations are often noisy. In this paper, we show that by introducing templates within the deep learning pipeline we can overcome these problems. The proposed framework, named ImplicitAtlas, represents a shape as a deformation field from a learned template field, where multiple templates could be integrated to improve the shape representation capacity at negligible computational cost. Extensive experiments on three medical shape datasets prove the superiority over current implicit representation methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_ImplicitAtlas_Learning_Deformable_Shape_Templates_in_Medical_Imaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_ImplicitAtlas_Learning_Deformable_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_ImplicitAtlas_Learning_Deformable_Shape_Templates_in_Medical_Imaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_ImplicitAtlas_Learning_Deformable_Shape_Templates_in_Medical_Imaging_CVPR_2022_paper.html | CVPR 2022 | null |
Unified Multivariate Gaussian Mixture for Efficient Neural Image Compression | Xiaosu Zhu, Jingkuan Song, Lianli Gao, Feng Zheng, Heng Tao Shen | Modeling latent variables with priors and hyperpriors is an essential problem in variational image compression. Formally, trade-off between rate and distortion is handled well if priors and hyperpriors precisely describe latent variables. Current practices only adopt univariate priors and process each variable individually. However, we find inter-correlations and intra-correlations exist when observing latent variables in a vectorized perspective. These findings reveal visual redundancies to improve rate-distortion performance and parallel processing ability to speed up compression. This encourages us to propose a novel vectorized prior. Specifically, a multivariate Gaussian mixture is proposed with means and covariances to be estimated. Then, a novel probabilistic vector quantization is utilized to effectively approximate means, and remaining covariances are further induced to a unified mixture and solved by cascaded estimation without context models involved. Furthermore, codebooks involved in quantization are extended to multi-codebooks for complexity reduction, which formulates an efficient compression procedure. Extensive experiments on benchmark datasets against state-of-the-art indicate our model has better rate-distortion performance and an impressive 3.18xcompression speed up, giving us the ability to perform real-time, high-quality variational image compression in practice. Our source code is publicly available at https://github.com/xiaosu-zhu/McQuic. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Unified_Multivariate_Gaussian_Mixture_for_Efficient_Neural_Image_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Unified_Multivariate_Gaussian_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10897 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Unified_Multivariate_Gaussian_Mixture_for_Efficient_Neural_Image_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Unified_Multivariate_Gaussian_Mixture_for_Efficient_Neural_Image_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
3D Photo Stylization: Learning To Generate Stylized Novel Views From a Single Image | Fangzhou Mu, Jian Wang, Yicheng Wu, Yin Li | Visual content creation has spurred a soaring interest given its applications in mobile photography and AR / VR. Style transfer and single-image 3D photography as two representative tasks have so far evolved independently. In this paper, we make a connection between the two, and address the challenging task of 3D photo stylization - generating stylized novel views from a single image given an arbitrary style. Our key intuition is that style transfer and view synthesis have to be jointly modeled. To this end, we propose a deep model that learns geometry-aware content features for stylization from a point cloud representation of the scene, resulting in high-quality stylized images that are consistent across views. Further, we introduce a novel training protocol to enable the learning using only 2D images. We demonstrate the superiority of our method via extensive qualitative and quantitative studies, and showcase key applications of our method in light of the growing demand for 3D content creation from 2D image assets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mu_3D_Photo_Stylization_Learning_To_Generate_Stylized_Novel_Views_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mu_3D_Photo_Stylization_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00169 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mu_3D_Photo_Stylization_Learning_To_Generate_Stylized_Novel_Views_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mu_3D_Photo_Stylization_Learning_To_Generate_Stylized_Novel_Views_From_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Visual Grounding With Visual-Linguistic Verification and Iterative Reasoning | Li Yang, Yan Xu, Chunfeng Yuan, Wei Liu, Bing Li, Weiming Hu | Visual grounding is a task to locate the target indicated by a natural language expression. Existing methods extend the generic object detection framework to this problem. They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text. However, modeling the visual features from these predefined locations may fail to fully exploit the visual context and attribute information in the text query, which limits their performance. In this paper, we propose a transformer-based framework for accurate visual grounding by establishing text-conditioned discriminative features and performing multi-stage cross-modal reasoning. Specifically, we develop a visual-linguistic verification module to focus the visual features on regions relevant to the textual descriptions while suppressing the unrelated areas. A language-guided feature encoder is also devised to aggregate the visual contexts of the target object to improve the object's distinctiveness. To retrieve the target from the encoded visual features, we further propose a multi-stage cross-modal decoder to iteratively speculate on the correlations between the image and text for accurate target localization. Extensive experiments on five widely used datasets validate the efficacy of our proposed components and demonstrate state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Improving_Visual_Grounding_With_Visual-Linguistic_Verification_and_Iterative_Reasoning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.00272 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Improving_Visual_Grounding_With_Visual-Linguistic_Verification_and_Iterative_Reasoning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Improving_Visual_Grounding_With_Visual-Linguistic_Verification_and_Iterative_Reasoning_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Learning for Space-Time Correspondence via Self-Cycle Consistency | Jeany Son | We propose a novel probabilistic method employing Bayesian Model Averaging and self-cycle regularization for spatio-temporal correspondence learning in videos within a self-supervised learning framework. Most existing methods for self-supervised correspondence learning suffer from noisy labels that come with the data for free, and the presence of occlusion exacerbates the problem. We tackle this issue within a probabilistic framework that handles model uncertainty inherent in the path selection problem built on a complete graph. We propose a self-cycle regularization to consider a cycle-consistency property on individual edges in order to prevent converging on noisy matching or trivial solutions. We also utilize a mixture of sequential Bayesian filters to estimate posterior distribution for targets. In addition, we present a domain contrastive loss to learn discriminative representation among videos. Our algorithm is evaluated on various datasets for video label propagation tasks including DAVIS2017, VIP and JHMDB, and shows outstanding performances compared to the state-of-the-art self-supervised learning based video correspondence algorithms. Moreover, our method converges significantly faster than previous methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Son_Contrastive_Learning_for_Space-Time_Correspondence_via_Self-Cycle_Consistency_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Son_Contrastive_Learning_for_Space-Time_Correspondence_via_Self-Cycle_Consistency_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Son_Contrastive_Learning_for_Space-Time_Correspondence_via_Self-Cycle_Consistency_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Robust Image-Based Rendering on Sparse Scene Geometry via Depth Completion | Yuqi Sun, Shili Zhou, Ri Cheng, Weimin Tan, Bo Yan, Lang Fu | Recent image-based rendering (IBR) methods usually adopt plenty of views to reconstruct dense scene geometry. However, the number of available views is limited in practice. When only few views are provided, the performance of these methods drops off significantly, as the scene geometry becomes sparse as well. Therefore, in this paper, we propose Sparse-IBRNet (SIBRNet) to perform robust IBR on sparse scene geometry by depth completion. The SIBRNet has two stages, geometry recovery (GR) stage and light blending (LB) stage. Specifically, GR stage takes sparse depth map and RGB as input to predict dense depth map by exploiting the correlation between two modals. As inaccuracy of the complete depth map may cause projection biases in the warping process, LB stage first uses a bias-corrected module (BCM) to rectify deviations, and then aggregates modified features from different views to render a novel view. Extensive experimental results demonstrate that our method performs best on sparse scene geometry than recent IBR methods, and it can generate better or comparable results as well when the geometric information is dense. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Learning_Robust_Image-Based_Rendering_on_Sparse_Scene_Geometry_via_Depth_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Learning_Robust_Image-Based_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Learning_Robust_Image-Based_Rendering_on_Sparse_Scene_Geometry_via_Depth_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Learning_Robust_Image-Based_Rendering_on_Sparse_Scene_Geometry_via_Depth_CVPR_2022_paper.html | CVPR 2022 | null |
Scale-Equivalent Distillation for Semi-Supervised Object Detection | Qiushan Guo, Yao Mu, Jianyu Chen, Tianqi Wang, Yizhou Yu, Ping Luo | Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based on self-training, i.e., generating hard pseudo-labels by a teacher model on unlabeled data as supervisory signals. Although they achieved certain success, the limited labeled data in semi-supervised learning scales up the challenges of object detection. We analyze the challenges these methods meet with the empirical experiment results. We find that the massive False Negative samples and inferior localization precision lack consideration. Besides, the large variance of object sizes and class imbalance (i.e., the extreme ratio between background and object) hinder the performance of prior arts. Further, we overcome these challenges by introducing a novel approach, Scale-Equivalent Distillation (SED), which is a simple yet effective end-to-end knowledge distillation framework robust to large object size variance and class imbalance. SED has several appealing benefits compared to the previous works. (1) SED imposes a consistency regularization to handle the large scale variance problem. (2) SED alleviates the noise problem from the False Negative samples and inferior localization precision. (3) A re-weighting strategy can implicitly screen the potential foreground regions of the unlabeled data to reduce the effect of class imbalance. Extensive experiments show that SED consistently outperforms the recent state-of-the-art methods on different datasets with significant margins. For example, it surpasses the supervised counterpart by more than 10 mAP when using 5% and 10% labeled data on MS-COCO. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Scale-Equivalent_Distillation_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Scale-Equivalent_Distillation_for_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Recurrent Variational Network: A Deep Learning Inverse Problem Solver Applied to the Task of Accelerated MRI Reconstruction | George Yiasemis, Jan-Jakob Sonke, Clarisa Sánchez, Jonas Teuwen | Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours. However, MRI suffers from very long acquisition times that make it susceptible to patient motion artifacts and limit its potential to deliver dynamic treatments. Conventional approaches such as Parallel Imaging and Compressed Sensing allow for an increase in MRI acquisition speed by reconstructing MR images from sub-sampled MRI data acquired using multiple receiver coils. Recent advancements in Deep Learning combined with Parallel Imaging and Compressed Sensing techniques have the potential to produce high-fidelity reconstructions from highly accelerated MRI data. In this work we present a novel Deep Learning-based Inverse Problem solver applied to the task of Accelerated MRI Reconstruction, called the Recurrent Variational Network (RecurrentVarNet), by exploiting the properties of Convolutional Recurrent Neural Networks and unrolled algorithms for solving Inverse Problems. The RecurrentVarNet consists of multiple recurrent blocks, each responsible for one iteration of the unrolled variational optimization scheme for solving the inverse problem of multi-coil Accelerated MRI Reconstruction. Contrary to traditional approaches, the optimization steps are performed in the observation domain (k-space) instead of the image domain. Each block of the RecurrentVarNet refines the observed k-space and comprises a data consistency term and a recurrent unit which takes as input a learned hidden state and the prediction of the previous block. Our proposed method achieves new state of the art qualitative and quantitative reconstruction results on 5-fold and 10-fold accelerated data from a public multi-coil brain dataset, outperforming previous conventional and deep learning-based approaches. Our code is publicly available at https://github.com/NKI-AI/direct. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yiasemis_Recurrent_Variational_Network_A_Deep_Learning_Inverse_Problem_Solver_Applied_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yiasemis_Recurrent_Variational_Network_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yiasemis_Recurrent_Variational_Network_A_Deep_Learning_Inverse_Problem_Solver_Applied_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yiasemis_Recurrent_Variational_Network_A_Deep_Learning_Inverse_Problem_Solver_Applied_CVPR_2022_paper.html | CVPR 2022 | null |
SelfD: Self-Learning Large-Scale Driving Policies From the Web | Jimuyang Zhang, Ruizhao Zhu, Eshed Ohn-Bar | Effectively utilizing the vast amounts of ego-centric navigation data that is freely available on the internet can advance generalized intelligent systems, i.e., to robustly scale across perspectives, platforms, environmental conditions, scenarios, and geographical locations. However, it is difficult to directly leverage such large amounts of unlabeled and highly diverse data for complex 3D reasoning and planning tasks. Consequently, researchers have primarily focused on its use for various auxiliary pixel- and image-level computer vision tasks that do not consider an ultimate navigational objective. In this work, we introduce SelfD, a framework for learning scalable driving by utilizing large amounts of online monocular images. Our key idea is to leverage iterative semi-supervised training when learning imitative agents from unlabeled data. To handle unconstrained viewpoints, scenes, and camera parameters, we train an image-based model that directly learns to plan in the Bird's Eye View (BEV) space. Next, we use unlabeled data to augment the decision-making knowledge and robustness of an initially trained model via self-training. In particular, we propose a pseudo-labeling step which enables making full use of highly diverse demonstration data through "hypothetical" planning-based data augmentation. We employ a large dataset of publicly available YouTube videos to train SelfD and comprehensively analyze its generalization benefits across challenging navigation scenarios. Without requiring any additional data collection or annotation efforts, SelfD demonstrates consistent improvements (by up to 24%) in driving performance evaluation on nuScenes, Argoverse, Waymo, and CARLA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_SelfD_Self-Learning_Large-Scale_Driving_Policies_From_the_Web_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_SelfD_Self-Learning_Large-Scale_Driving_Policies_From_the_Web_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_SelfD_Self-Learning_Large-Scale_Driving_Policies_From_the_Web_CVPR_2022_paper.html | CVPR 2022 | null |
"The Pedestrian Next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping | Avishkar Saha, Oscar Mendez, Chris Russell, Richard Bowden | Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation. However, they show an increase in localization error with distance from the camera. While such an increase in error is entirely expected - localization is harder at distance - much of the drop in performance can be attributed to the cues used by current texture-based models, in particular, they make heavy use of object-ground intersections (such as shadows), which become increasingly sparse and uncertain for distant objects. In this work, we address these shortcomings in BEV-mapping by learning the spatial relationship between objects in a scene. We propose a graph neural network which predicts BEV objects from a monocular image by spatially reasoning about an object within the context of other objects. Our approach sets a new state-of-the-art in BEV estimation from monocular images across three large-scale datasets, including a 50% relative improvement for objects on nuScenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Saha_The_Pedestrian_Next_to_the_Lamppost_Adaptive_Object_Graphs_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Saha_The_Pedestrian_Next_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Saha_The_Pedestrian_Next_to_the_Lamppost_Adaptive_Object_Graphs_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Saha_The_Pedestrian_Next_to_the_Lamppost_Adaptive_Object_Graphs_for_CVPR_2022_paper.html | CVPR 2022 | null |
Attribute Group Editing for Reliable Few-Shot Image Generation | Guanqi Ding, Xinzhe Han, Shuhui Wang, Shuzhe Wu, Xin Jin, Dandan Tu, Qingming Huang | Few-shot image generation is a challenging task even using the state-of-the-art Generative Adversarial Networks (GANs). Due to the unstable GAN training process and the limited training data, the generated images are often of low quality and low diversity. In this work, we propose a new "editing-based" method, i.e., Attribute Group Editing (AGE), for few-shot image generation. The basic assumption is that any image is a collection of attributes and the editing direction for a specific attribute is shared across all categories. AGE examines the internal representation learned in GANs and identifies semantically meaningful directions. Specifically, the class embedding, i.e., the mean vector of the latent codes from a specific category, is used to represent the category-relevant attributes, and the category-irrelevant attributes are learned globally by Sparse Dictionary Learning on the difference between the sample embedding and the class embedding. Given a GAN well trained on seen categories, diverse images of unseen categories can be synthesized through editing category-irrelevant attributes while keeping category-relevant attributes unchanged. Without re-training the GAN, AGE is capable of not only producing more realistic and diverse images for downstream visual applications with limited data but achieving controllable image editing with interpretable category-irrelevant directions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Attribute_Group_Editing_for_Reliable_Few-Shot_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Attribute_Group_Editing_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08422 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Attribute_Group_Editing_for_Reliable_Few-Shot_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Attribute_Group_Editing_for_Reliable_Few-Shot_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Surpassing the Human Accuracy: Detecting Gallbladder Cancer From USG Images With Curriculum Learning | Soumen Basu, Mayank Gupta, Pratyaksha Rana, Pankaj Gupta, Chetan Arora | We explore the potential of CNN-based models for gallbladder cancer (GBC) detection from ultrasound (USG) images as no prior study is known. USG is the most common diagnostic modality for GB diseases due to its low cost and accessibility. However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor. Our exhaustive study of state-of-the-art (SOTA) image classification techniques for the problem reveals that they often fail to learn the salient GB region due to the presence of shadows in the USG images. SOTA object detection techniques also achieve low accuracy because of spurious textures due to noise or adjacent organs. We propose GBCNet to tackle the challenges in our problem. GBCNet first extracts the regions of interest (ROIs) by detecting the GB (and not the cancer), and then uses a new multi-scale, second-order pooling architecture specializing in classifying GBC. To effectively handle spurious textures, we propose a curriculum inspired by human visual acuity, which reduces the texture biases in GBCNet. Experimental results demonstrate that GBCNet significantly outperforms SOTA CNN models, as well as the expert radiologists. Our technical innovations are generic to other USG image analysis tasks as well. Hence, as a validation, we also show the efficacy of GBCNet in detecting breast cancer from USG images. Project page with source code, trained models, and data is available at https://gbc-iitd.github.io/gbcnet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Basu_Surpassing_the_Human_Accuracy_Detecting_Gallbladder_Cancer_From_USG_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Basu_Surpassing_the_Human_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.11433 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Basu_Surpassing_the_Human_Accuracy_Detecting_Gallbladder_Cancer_From_USG_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Basu_Surpassing_the_Human_Accuracy_Detecting_Gallbladder_Cancer_From_USG_Images_CVPR_2022_paper.html | CVPR 2022 | null |
CroMo: Cross-Modal Learning for Monocular Depth Estimation | Yannick Verdié, Jifei Song, Barnabé Mas, Benjamin Busam, Ales̆ Leonardis, Steven McDonagh | Learning-based depth estimation has witnessed recent progress in multiple directions; from self-supervision using monocular video to supervised methods offering highest accuracy. Complementary to supervision, further boosts to performance and robustness are gained by combining information from multiple signals. In this paper we systematically investigate key trade-offs associated with sensor and modality design choices as well as related model training strategies. Our study leads us to a new method, capable of connecting modality-specific advantages from polarisation, Time-of-Flight and structured-light inputs. We propose a novel pipeline capable of estimating depth from monocular polarisation for which we evaluate various training signals. The inversion of differentiable analytic models thereby connects scene geometry with polarisation and ToF signals and enables self-supervised and cross-modal learning. In the absence of existing multimodal datasets, we examine our approach with a custom-made multi-modal camera rig and collect CroMo; the first dataset to consist of synchronized stereo polarisation, indirect ToF and structured-light depth, captured at video rates. Extensive experiments on challenging video scenes confirm both qualitative and quantitative pipeline advantages where we are able to outperform competitive monocular depth estimation methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Verdie_CroMo_Cross-Modal_Learning_for_Monocular_Depth_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Verdie_CroMo_Cross-Modal_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Verdie_CroMo_Cross-Modal_Learning_for_Monocular_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Verdie_CroMo_Cross-Modal_Learning_for_Monocular_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Object Detection From Audio-Visual Correspondence | Triantafyllos Afouras, Yuki M. Asano, Francois Fagan, Andrea Vedaldi, Florian Metze | We tackle the problem of learning object detectors without supervision. Differently from weakly-supervised object detection, we do not assume image-level class labels. Instead, we extract a supervisory signal from audio-visual data, using the audio component to "teach" the object detector. While this problem is related to sound source localisation, it is considerably harder because the detector must classify the objects by type, enumerate each instance of the object, and do so even when the object is silent. We tackle this problem by first designing a self-supervised framework with a contrastive objective that jointly learns to classify and localise objects. Then, without using any supervision, we simply use these self-supervised labels and boxes to train an image-based object detector. With this, we outperform previous unsupervised and weakly-supervised detectors for the task of object detection and sound source localization. We also show that we can align this detector to ground-truth classes with as little as one label per pseudo-class, and show how our method can learn to detect generic objects that go beyond instruments, such as airplanes and cats. | https://openaccess.thecvf.com/content/CVPR2022/papers/Afouras_Self-Supervised_Object_Detection_From_Audio-Visual_Correspondence_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.06401 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Afouras_Self-Supervised_Object_Detection_From_Audio-Visual_Correspondence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Afouras_Self-Supervised_Object_Detection_From_Audio-Visual_Correspondence_CVPR_2022_paper.html | CVPR 2022 | null |
Autofocus for Event Cameras | Shijie Lin, Yinqiang Zhang, Lei Yu, Bin Zhou, Xiaowei Luo, Jia Pan | Focus control (FC) is crucial for cameras to capture sharp images in challenging real-world scenarios. The autofocus (AF) facilitates the FC by automatically adjusting the focus settings. However, due to the lack of effective AF methods for the recently introduced event cameras, their FC still relies on naive AF like manual focus adjustments, leading to poor adaptation in challenging real-world conditions. In particular, the inherent differences between event and frame data in terms of sensing modality, noise, temporal resolutions, etc., bring many challenges in designing an effective AF method for event cameras. To address these challenges, we develop a novel event-based autofocus framework consisting of an event-specific focus measure called event rate (ER) and a robust search strategy called event-based golden search (EGS). To verify the performance of our method, we have collected an event-based autofocus dataset (EAD) containing well-synchronized frames, events, and focal positions in a wide variety of challenging scenes with severe lighting and motion conditions. The experiments on this dataset and additional real-world scenarios demonstrated the superiority of our method over state-of-the-art approaches in terms of efficiency and accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Autofocus_for_Event_Cameras_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Autofocus_for_Event_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.12321 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Autofocus_for_Event_Cameras_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Autofocus_for_Event_Cameras_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Multiple Adverse Weather Removal via Two-Stage Knowledge Learning and Multi-Contrastive Regularization: Toward a Unified Model | Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, Sy-Yen Kuo | In this paper, an ill-posed problem of multiple adverse weather removal is investigated. Our goal is to train a model with a 'unified' architecture and only one set of pretrained weights that can tackle multiple types of adverse weathers such as haze, snow, and rain simultaneously. To this end, a two-stage knowledge learning mechanism including knowledge collation (KC) and knowledge examination (KE) based on a multi-teacher and student architecture is proposed. At the KC, the student network aims to learn the comprehensive bad weather removal problem from multiple well-trained teacher networks where each of them is specialized in a specific bad weather removal problem. To accomplish this process, a novel collaborative knowledge transfer is proposed. At the KE, the student model is trained without the teacher networks and examined by challenging pixel loss derived by the ground truth. Moreover, to improve the performance of our training framework, a novel loss function called multi-contrastive knowledge regularization (MCR) loss is proposed. Experiments on several datasets show that our student model can achieve promising performance on different bad weather removal tasks simultaneously. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Learning_Multiple_Adverse_Weather_Removal_via_Two-Stage_Knowledge_Learning_and_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Learning_Multiple_Adverse_Weather_Removal_via_Two-Stage_Knowledge_Learning_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Learning_Multiple_Adverse_Weather_Removal_via_Two-Stage_Knowledge_Learning_and_CVPR_2022_paper.html | CVPR 2022 | null |
Polymorphic-GAN: Generating Aligned Samples Across Multiple Domains With Learned Morph Maps | Seung Wook Kim, Karsten Kreis, Daiqing Li, Antonio Torralba, Sanja Fidler | Modern image generative models show remarkable sample quality when trained on a single domain or class of objects. In this work, we introduce a generative adversarial network that can simultaneously generate aligned image samples from multiple related domains. We leverage the fact that a variety of object classes share common attributes, with certain geometric differences. We propose Polymorphic-GAN which learns shared features across all domains and a per-domain morph layer to morph shared features according to each domain. In contrast to previous works, our framework allows simultaneous modelling of images with highly varying geometries, such as images of human faces, painted and artistic faces, as well as multiple different animal faces. We demonstrate that our model produces aligned samples for all domains and show how it can be used for applications such as segmentation transfer and cross-domain image editing, as well as training in low-data regimes. Additionally, we apply our Polymorphic-GAN on image-to-image translation tasks and show that we can greatly surpass previous approaches in cases where the geometric differences between domains are large. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Polymorphic-GAN_Generating_Aligned_Samples_Across_Multiple_Domains_With_Learned_Morph_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Polymorphic-GAN_Generating_Aligned_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Polymorphic-GAN_Generating_Aligned_Samples_Across_Multiple_Domains_With_Learned_Morph_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Polymorphic-GAN_Generating_Aligned_Samples_Across_Multiple_Domains_With_Learned_Morph_CVPR_2022_paper.html | CVPR 2022 | null |
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond | Qibing Ren, Qingquan Bao, Runzhong Wang, Junchi Yan | Despite the recent breakthrough of high accuracy deep graph matching (GM) over visual images, the robustness of deep GM models is rarely studied which yet has been revealed an important issue in modern deep nets, ranging from image recognition to graph learning tasks. We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models. Accordingly, we propose our defense strategy, namely Appearance and Structure Aware Robust Graph Matching (ASAR-GM). Specifically, orthogonal to de facto adversarial training (AT), we devise the Appearance Aware Regularizer (AAR) on those appearance-similar keypoints between graphs that are likely to confuse. Experimental results show that our ASAR-GM achieves better robustness compared to AT. Moreover, our locality attack can serve as a data augmentation technique, which boosts the state-of-the-art GM models even on the clean test dataset. Code is available at https://github.com/Thinklab-SJTU/RobustMatch. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_Appearance_and_Structure_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
Super-Fibonacci Spirals: Fast, Low-Discrepancy Sampling of SO(3) | Marc Alexa | Super-Fibonacci spirals are an extension of Fibonacci spirals, enabling fast generation of an arbitrary but fixed number of 3D orientations. The algorithm is simple and fast. A comprehensive evaluation comparing to other methods shows that the generated sets of orientations have low discrepancy, minimal spurious components in the power spectrum, and almost identical Voronoi volumes. This makes them useful for a variety of applications in vision, robotics, machine learning, and in particular Monte Carlo sampling. | https://openaccess.thecvf.com/content/CVPR2022/papers/Alexa_Super-Fibonacci_Spirals_Fast_Low-Discrepancy_Sampling_of_SO3_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alexa_Super-Fibonacci_Spirals_Fast_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alexa_Super-Fibonacci_Spirals_Fast_Low-Discrepancy_Sampling_of_SO3_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alexa_Super-Fibonacci_Spirals_Fast_Low-Discrepancy_Sampling_of_SO3_CVPR_2022_paper.html | CVPR 2022 | null |
TrackFormer: Multi-Object Tracking With Transformers | Tim Meinhardt, Alexander Kirillov, Laura Leal-Taixé, Christoph Feichtenhofer | The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about track initialization, identity, and spatio-temporal trajectories. We formulate this task as a frame-to-frame set prediction problem and introduce TrackFormer, an end-to-end trainable MOT approach based on an encoder-decoder Transformer architecture. Our model achieves data association between frames via attention by evolving a set of track predictions through a video sequence. The Transformer decoder initializes new tracks from static object queries and autoregressively follows existing tracks in space and time with the conceptually new and identity preserving track queries. Both query types benefit from self- and encoder-decoder attention on global frame-level features, thereby omitting any additional graph optimization or modeling of motion and/or appearance. TrackFormer introduces a new tracking-by-attention paradigm and while simple in its design is able to achieve state-of-the-art performance on the task of multi-object tracking (MOT17) and segmentation (MOTS20). The code is available at https://github.com/timmeinhardt/trackformer | https://openaccess.thecvf.com/content/CVPR2022/papers/Meinhardt_TrackFormer_Multi-Object_Tracking_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Meinhardt_TrackFormer_Multi-Object_Tracking_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Meinhardt_TrackFormer_Multi-Object_Tracking_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Meinhardt_TrackFormer_Multi-Object_Tracking_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
L-Verse: Bidirectional Generation Between Image and Text | Taehoon Kim, Gwangmo Song, Sihaeng Lee, Sangyun Kim, Yewon Seo, Soonyoung Lee, Seung Hwan Kim, Honglak Lee, Kyunghoon Bae | Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to make a raw RGB image into a sequence of feature vectors. To better leverage the correlation between image and text, we propose L-Verse, a novel architecture consisting of feature-augmented variational autoencoder (AugVAE) and bidirectional auto-regressive transformer (BiART) for image-to-text and text-to-image generation. Our AugVAE shows the state-of-the-art reconstruction performance on ImageNet1K validation set, along with the robustness to unseen images in the wild. Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target. L-Verse can be directly used for image-to-text or text-to-image generation without any finetuning or extra object detection framework. In quantitative and qualitative experiments, L-Verse shows impressive results against previous methods in both image-to-text and text-to-image generation on MS-COCO Captions. We furthermore assess the scalability of L-Verse architecture on Conceptual Captions and present the initial result of bidirectional vision-language representation learning on general domain. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_L-Verse_Bidirectional_Generation_Between_Image_and_Text_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_L-Verse_Bidirectional_Generation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_L-Verse_Bidirectional_Generation_Between_Image_and_Text_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_L-Verse_Bidirectional_Generation_Between_Image_and_Text_CVPR_2022_paper.html | CVPR 2022 | null |
PanopticDepth: A Unified Framework for Depth-Aware Panoptic Segmentation | Naiyu Gao, Fei He, Jian Jia, Yanhu Shan, Haoyang Zhang, Xin Zhao, Kaiqi Huang | This paper presents a unified framework for depth-aware panoptic segmentation (DPS), which aims to reconstruct 3D scene with instance-level semantics from one single image. Prior works address this problem by simply adding a dense depth regression head to panoptic segmentation (PS) networks, resulting in two independent task branches. This neglects the mutually-beneficial relations between these two tasks, thus failing to exploit handy instance-level semantic cues to boost depth accuracy while also producing sub-optimal depth maps. To overcome these limitations, we propose a unified framework for the DPS task by applying a dynamic convolution technique to both the PS and depth prediction tasks. Specifically, instead of predicting depth for all pixels at a time, we generate instance-specific kernels to predict depth and segmentation masks for each instance. Moreover, leveraging the instance-wise depth estimation scheme, we add additional instance-level depth cues to assist with supervising the depth learning via a new depth loss. Extensive experiments on Cityscapes-DPS and SemKITTI-DPS show the effectiveness and promise of our method. We hope our unified solution to DPS can lead a new paradigm in this area. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_PanopticDepth_A_Unified_Framework_for_Depth-Aware_Panoptic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2206.00468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_PanopticDepth_A_Unified_Framework_for_Depth-Aware_Panoptic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_PanopticDepth_A_Unified_Framework_for_Depth-Aware_Panoptic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
3D Shape Reconstruction From 2D Images With Disentangled Attribute Flow | Xin Wen, Junsheng Zhou, Yu-Shen Liu, Hua Su, Zhen Dong, Zhizhong Han | Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, most of the previous methods still struggle to extract semantic attributes for 3D reconstruction task. Since the semantic attributes of a single image are usually implicit and entangled with each other, it is still challenging to reconstruct 3D shape with detailed semantic structures represented by the input image. To address this problem, we propose 3DAttriFlow to disentangle and extract semantic attributes through different semantic levels in the input images. These disentangled semantic attributes will be integrated into the 3D shape reconstruction process, which can provide definite guidance to the reconstruction of specific attribute on 3D shape. As a result, the 3D decoder can explicitly capture high-level semantic features at the bottom of the network, and utilize low-level features at the top of the network, which allows to reconstruct more accurate 3D shapes. Note that the explicit disentangling is learned without extra labels, where the only supervision used in our training is the input image and its corresponding 3D shape. Our comprehensive experiments on ShapeNet dataset demonstrate that 3DAttriFlow outperforms the state-of-the-art shape reconstruction methods, and we also validate its generalization ability on shape completion task. Code is available at https://github.com/junshengzhou/3DAttriFlow. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wen_3D_Shape_Reconstruction_From_2D_Images_With_Disentangled_Attribute_Flow_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15190 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wen_3D_Shape_Reconstruction_From_2D_Images_With_Disentangled_Attribute_Flow_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wen_3D_Shape_Reconstruction_From_2D_Images_With_Disentangled_Attribute_Flow_CVPR_2022_paper.html | CVPR 2022 | null |
Feature Statistics Mixing Regularization for Generative Adversarial Networks | Junho Kim, Yunjey Choi, Youngjung Uh | In generative adversarial networks, improving discriminators is one of the key components for generation performance. As image classifiers are biased toward texture and debiasing improves accuracy, we investigate 1) if the discriminators are biased, and 2) if debiasing the discriminators will improve generation performance. Indeed, we find empirical evidence that the discriminators are sensitive to the style (e.g., texture and color) of images. As a remedy, we propose feature statistics mixing regularization (FSMR) that encourages the discriminator's prediction to be invariant to the styles of input images. Specifically, we generate a mixed feature of an original and a reference image in the discriminator's feature space and we apply regularization so that the prediction for the mixed feature is consistent with the prediction for the original image. We conduct extensive experiments to demonstrate that our regularization leads to reduced sensitivity to style and consistently improves the performance of various GAN architectures on nine datasets. In addition, adding FSMR to recently proposed augmentation-based GAN methods further improves image quality. Our code is available at https://github.com/naver-ai/FSMR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Feature_Statistics_Mixing_Regularization_for_Generative_Adversarial_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Feature_Statistics_Mixing_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04120 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Feature_Statistics_Mixing_Regularization_for_Generative_Adversarial_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Feature_Statistics_Mixing_Regularization_for_Generative_Adversarial_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Learn and Remember Super Long Multi-Domain Task Sequence | Zhenyi Wang, Li Shen, Tiehang Duan, Donglin Zhan, Le Fang, Mingchen Gao | Catastrophic forgetting (CF) frequently occurs when learning with non-stationary data distribution. The CF issue remains nearly unexplored and is more challenging when meta-learning on a sequence of domains (datasets), called sequential domain meta-learning (SDML). In this work, we propose a simple yet effective learning to learn approach, i.e., meta optimizer, to mitigate the CF problem in SDML. We first apply the proposed meta optimizer to the simplified setting of SDML, domain-aware meta-learning, where the domain labels and boundaries are known during the learning process. We propose dynamically freezing the network and incorporating it with the proposed meta optimizer by considering the domain nature during meta training. In addition, we extend the meta optimizer to the more general setting of SDML, domain-agnostic meta-learning, where domain labels and boundaries are unknown during the learning process. We propose a domain shift detection technique to capture latent domain change and equip the meta optimizer with it to work in this setting. The proposed meta optimizer is versatile and can be easily integrated with several existing meta-learning algorithms. Finally, we construct a challenging and large-scale benchmark consisting of 10 heterogeneous domains with a super long task sequence consisting of 100K tasks. We perform extensive experiments on the proposed benchmark for both settings and demonstrate the effectiveness of our proposed method, outperforming current strong baselines by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learning_To_Learn_and_Remember_Super_Long_Multi-Domain_Task_Sequence_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Learning_To_Learn_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_To_Learn_and_Remember_Super_Long_Multi-Domain_Task_Sequence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_To_Learn_and_Remember_Super_Long_Multi-Domain_Task_Sequence_CVPR_2022_paper.html | CVPR 2022 | null |
OpenTAL: Towards Open Set Temporal Action Localization | Wentao Bao, Qi Yu, Yu Kong | Temporal Action Localization (TAL) has experienced remarkable success under the supervised learning paradigm. However, existing TAL methods are rooted in the closed set assumption, which cannot handle the inevitable unknown actions in open-world scenarios. In this paper, we, for the first time, step toward the Open Set TAL (OSTAL) problem and propose a general framework OpenTAL based on Evidential Deep Learning (EDL). Specifically, the OpenTAL consists of uncertainty-aware action classification, actionness prediction, and temporal location regression. With the proposed importance-balanced EDL method, classification uncertainty is learned by collecting categorical evidence majorly from important samples. To distinguish the unknown actions from background video frames, the actionness is learned by the positive-unlabeled learning. The classification uncertainty is further calibrated by leveraging the guidance from the temporal localization quality. The OpenTAL is general to enable existing TAL models for open set scenarios, and experimental results on THUMOS14 and ActivityNet1.3 benchmarks show the effectiveness of our method. The code and pre-trained models are released at https://www.rit.edu/actionlab/opental. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bao_OpenTAL_Towards_Open_Set_Temporal_Action_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bao_OpenTAL_Towards_Open_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05114 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_OpenTAL_Towards_Open_Set_Temporal_Action_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bao_OpenTAL_Towards_Open_Set_Temporal_Action_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Urban Radiance Fields | Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan, Jonathan T. Barron, Andrea Tagliasacchi, Thomas Funkhouser, Vittorio Ferrari | The goal of this work is to perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments (e.g., Street View). Given a sequence of posed RGB images and lidar sweeps acquired by cameras and scanners moving through an outdoor scene, we produce a model from which 3D surfaces can be extracted and novel RGB images can be synthesized. Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings, with new methods for leveraging asynchronously captured lidar data, for addressing exposure variation between captured images, and for leveraging predicted image segmentations to supervise densities on rays pointing at the sky. Each of these three extensions provides significant performance improvements in experiments on Street View data. Our system produces state-of-the-art 3D surface reconstructions and synthesizes higher quality novel views in comparison to both traditional methods (e.g. COLMAP) and recent neural representations (e.g. Mip-NeRF). | https://openaccess.thecvf.com/content/CVPR2022/papers/Rematas_Urban_Radiance_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rematas_Urban_Radiance_Fields_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14643 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rematas_Urban_Radiance_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rematas_Urban_Radiance_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection | Liang Chen, Yong Zhang, Yibing Song, Lingqiao Liu, Jue Wang | Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging when one tries to generalize the detector to forgeries created by unseen methods in the training dataset. This work addresses the generalizable deepfake detection from a simple principle: a generalizable representation should be sensitive to diverse types of forgeries. Following this principle, we propose to enrich the "diversity" of forgeries by synthesizing augmented forgeries with a pool of forgery configurations and strengthen the "sensitivity" to the forgeries by enforcing the model to predict the forgery configurations. To effectively explore the large forgery augmentation space, we further propose to use the adversarial training strategy to dynamically synthesize the most challenging forgeries to the current model. Through extensive experiments, we show that the proposed strategies are surprisingly effective (see Figure 1), and they could achieve superior performance than the current state-of the-art methods. Code is available at https://github.com/liangchen527/SLADD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Self-Supervised_Learning_of_Adversarial_Example_Towards_Good_Generalizations_for_Deepfake_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Self-Supervised_Learning_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12208 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Self-Supervised_Learning_of_Adversarial_Example_Towards_Good_Generalizations_for_Deepfake_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Self-Supervised_Learning_of_Adversarial_Example_Towards_Good_Generalizations_for_Deepfake_CVPR_2022_paper.html | CVPR 2022 | null |
Domain-Agnostic Prior for Transfer Semantic Segmentation | Xinyue Huo, Lingxi Xie, Hengtong Hu, Wengang Zhou, Houqiang Li, Qi Tian | Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. The key difficulty lies in defining a common property between the source and target domains so that the source-domain features can align with the target-domain semantics. In this paper, we present a simple and effective mechanism that regularizes cross-domain representation learning with a domain-agnostic prior (DAP) that constrains the features extracted from source and target domains to align with a domain-agnostic space. In practice, this is easily implemented as an extra loss term that requires a little extra costs. In the standard evaluation protocol of transferring synthesized data to real data, we validate the effectiveness of different types of DAP, especially one borrowed from a text embedding model that shows favorable performance beyond the state-of-the-art UDA approaches in terms of segmentation accuracy. Our research reveals that much room is left for designing better proxies for UDA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huo_Domain-Agnostic_Prior_for_Transfer_Semantic_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huo_Domain-Agnostic_Prior_for_Transfer_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huo_Domain-Agnostic_Prior_for_Transfer_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-Learning | Arnav Chavan, Rishabh Tiwari, Udbhav Bamba, Deepak K. Gupta | Gradient based meta-learning methods are prone to overfit on the meta-training set, and this behaviour is more prominent with large and complex networks. Moreover, large networks restrict the application of meta-learning models on low-power edge devices. While choosing smaller networks avoid these issues to a certain extent, it affects the overall generalization leading to reduced performance. Clearly, there is an approximately optimal choice of network architecture that is best suited for every meta-learning problem, however, identifying it beforehand is not straightforward. In this paper, we present MetaDOCK, a task-specific dynamic kernel selection strategy for designing compressed CNN models that generalize well on unseen tasks in meta-learning. Our method is based on the hypothesis that for a given set of similar tasks, not all kernels of the network are needed by each individual task. Rather, each task uses only a fraction of the kernels, and the selection of the kernels per task can be learnt dynamically as a part of the inner update steps. MetaDOCK compresses the meta-model as well as the task-specific inner models, thus providing significant reduction in model size for each task, and through constraining the number of active kernels for every task, it implicitly mitigates the issue of meta-overfitting. We show that for the same inference budget, pruned versions of large CNN models obtained using our approach consistently outperform the conventional choices of CNN models. MetaDOCK couples well with popular meta-learning approaches such as iMAML. The efficacy of our method is validated on CIFAR-fs and mini-ImageNet datasets, and we have observed that our approach can provide improvements in model accuracy of up to 2% on standard meta-learning benchmark, while reducing the model size by more than 75%. Our code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chavan_Dynamic_Kernel_Selection_for_Improved_Generalization_and_Memory_Efficiency_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chavan_Dynamic_Kernel_Selection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chavan_Dynamic_Kernel_Selection_for_Improved_Generalization_and_Memory_Efficiency_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chavan_Dynamic_Kernel_Selection_for_Improved_Generalization_and_Memory_Efficiency_in_CVPR_2022_paper.html | CVPR 2022 | null |
Ego4D: Around the World in 3,000 Hours of Egocentric Video | Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina González, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jáchym Kolář, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbeláez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, Jitendra Malik | We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household, outdoor, workplace, leisure, etc.) captured by 931 unique camera wearers from 74 worldwide locations and 9 different countries. The approach to collection is designed to uphold rigorous privacy and ethics standards, with consenting participants and robust de-identification procedures where relevant. Ego4D dramatically expands the volume of diverse egocentric video footage publicly available to the research community. Portions of the video are accompanied by audio, 3D meshes of the environment, eye gaze, stereo, and/or synchronized videos from multiple egocentric cameras at the same event. Furthermore, we present a host of new benchmark challenges centered around understanding the first-person visual experience in the past (querying an episodic memory), present (analyzing hand-object manipulation, audio-visual conversation, and social interactions), and future (forecasting activities). By publicly sharing this massive annotated dataset and benchmark suite, we aim to push the frontier of first-person perception. Project page: https://ego4d-data.org/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Grauman_Ego4D_Around_the_World_in_3000_Hours_of_Egocentric_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Grauman_Ego4D_Around_the_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Grauman_Ego4D_Around_the_World_in_3000_Hours_of_Egocentric_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Grauman_Ego4D_Around_the_World_in_3000_Hours_of_Egocentric_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Differentially Private Federated Learning With Local Regularization and Sparsification | Anda Cheng, Peisong Wang, Xi Sheryl Zhang, Jian Cheng | User-level differential privacy (DP) provides certifiable privacy guarantees to the information that is specific to any user's data in federated learning. Existing methods that ensure user-level DP come at the cost of severe accuracy decrease. In this paper, we study the cause of model performance degradation in federated learning with user-level DP guarantee. We find the key to solving this issue is to naturally restrict the norm of local updates before executing operations that guarantee DP. To this end, we propose two techniques, Bounded Local Update Regularization and Local Update Sparsification, to increase model quality without sacrificing privacy. We provide theoretical analysis on the convergence of our framework and give rigorous privacy guarantees. Extensive experiments show that our framework significantly improves the privacy-utility trade-off over the state-of-the-arts for federated learning with user-level DP guarantee. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Differentially_Private_Federated_Learning_With_Local_Regularization_and_Sparsification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cheng_Differentially_Private_Federated_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03106 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Differentially_Private_Federated_Learning_With_Local_Regularization_and_Sparsification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Differentially_Private_Federated_Learning_With_Local_Regularization_and_Sparsification_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis | Yucheng Tang, Dong Yang, Wenqi Li, Holger R. Roth, Bennett Landman, Daguang Xu, Vishwesh Nath, Ali Hatamizadeh | Vision Transformers (ViT)s have shown great performance in self-supervised learning of global and local representations that can be transferred to downstream applications. Inspired by these results, we introduce a novel self-supervised learning framework with tailored proxy tasks for medical image analysis. Specifically, we propose: (i) a new 3D transformer-based model, dubbed Swin UNEt TRansformers (Swin UNETR), with a hierarchical encoder for self-supervised pre-training; (ii) tailored proxy tasks for learning the underlying pattern of human anatomy. We demonstrate successful pre-training of the proposed model on 5050 publicly available computed tomography (CT) images from various body organs. The effectiveness of our approach is validated by fine-tuning the pre-trained models on the Beyond the Cranial Vault (BTCV) Segmentation Challenge with 13 abdominal organs and segmentation tasks from the Medical Segmentation Decathlon (MSD) dataset. Our model is currently the state-of-the-art on the public test leaderboards of both MSD and BTCV datasets. Code: https://monai.io/research/swin-unetr. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Self-Supervised_Pre-Training_of_Swin_Transformers_for_3D_Medical_Image_Analysis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Self-Supervised_Pre-Training_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14791 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Self-Supervised_Pre-Training_of_Swin_Transformers_for_3D_Medical_Image_Analysis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Self-Supervised_Pre-Training_of_Swin_Transformers_for_3D_Medical_Image_Analysis_CVPR_2022_paper.html | CVPR 2022 | null |
Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification | Chao Wu, Wenhang Ge, Ancong Wu, Xiaobin Chang | To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training samples could be unavailable under the ISolated Camera Supervised (ISCS) setting, e.g., a surveillance system deployed across distant scenes. To handle this challenging problem, a new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Specifically, the feature encoder and generator are end-to-end optimized under a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint learning procedure raises concern on the stability of generative model training. Therefore, a new feature generator, Sigma-Regularized Conditional Variational Autoencoder (Sigma-Reg CVAE), is proposed with theoretical and experimental analysis on its robustness. Extensive experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Camera-Conditioned_Stable_Feature_Generation_for_Isolated_Camera_Supervised_Person_Re-IDentification_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15210 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Camera-Conditioned_Stable_Feature_Generation_for_Isolated_Camera_Supervised_Person_Re-IDentification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Camera-Conditioned_Stable_Feature_Generation_for_Isolated_Camera_Supervised_Person_Re-IDentification_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Semantic Segmentation Using Out-of-Distribution Data | Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, Sungroh Yoon | Weakly supervised semantic segmentation (WSSS) methods are often built on pixel-level localization maps obtained from a classifier. However, training on class labels only, classifiers suffer from the spurious correlation between foreground and background cues (e.g. train and rail), fundamentally bounding the performance of WSSS methods. There have been previous endeavors to address this issue with additional supervision. We propose a novel source of information to distinguish foreground from the background: Out-of-Distribution (OoD) data, or images devoid of foreground object classes. In particular, we utilize the hard OoDs that the classifier is likely to make false-positive predictions. These samples typically carry key visual features on the background (e.g. rail) that the classifiers often confuse as the foreground class (e.g. train). These background cues let classifiers correctly suppress spurious background cues, resulting in an improved pixel-wise map from the classifier. From the cost point of view, acquiring such hard OoDs does not require an extensive amount of annotation efforts; it only incurs a few additional image-level labeling costs on top of the original efforts to collect the weak training set with the image labels. We propose a method, W-OoD, for utilizing the hard OoDs. W-OoD achieves state-of-the-art performance on Pascal VOC 2012. The code is available at: https://github.com/naver-ai/w-ood. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Weakly_Supervised_Semantic_Segmentation_Using_Out-of-Distribution_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Weakly_Supervised_Semantic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03860 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Weakly_Supervised_Semantic_Segmentation_Using_Out-of-Distribution_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Weakly_Supervised_Semantic_Segmentation_Using_Out-of-Distribution_Data_CVPR_2022_paper.html | CVPR 2022 | null |
Point-Level Region Contrast for Object Detection Pre-Training | Yutong Bai, Xinlei Chen, Alexander Kirillov, Alan Yuille, Alexander C. Berg | In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection. This approach is motivated by the two key factors in detection: localization and recognition. While accurate localization favors models that operate at the pixel- or point-level, correct recognition typically relies on a more holistic, region-level view of objects. Incorporating this perspective in pre-training, our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality, and further enables us to implicitly improve initial region assignments via online knowledge distillation during training. Both advantages are important when dealing with imperfect regions encountered in the unsupervised setting. Experiments show point-level region contrast improves on state-of-the-art pre-training methods for object detection and segmentation across multiple tasks and datasets, and we provide extensive ablation studies and visualizations to aid understanding. Code will be made available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_Point-Level_Region_Contrast_for_Object_Detection_Pre-Training_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bai_Point-Level_Region_Contrast_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.04639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Point-Level_Region_Contrast_for_Object_Detection_Pre-Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Point-Level_Region_Contrast_for_Object_Detection_Pre-Training_CVPR_2022_paper.html | CVPR 2022 | null |
Upright-Net: Learning Upright Orientation for 3D Point Cloud | Xufang Pang, Feng Li, Ning Ding, Xiaopin Zhong | A mass of experiments shows that the pose of the input 3D models exerts a tremendous influence on automatic 3D shape analysis. In this paper, we propose Upright-Net, a deep-learning-based approach for estimating the upright orientation of 3D point clouds. Based on a well-known postulate of design states that "form ever follows function", we treat the natural base of an object as a common functional structure, which supports the object in a most commonly seen pose following a set of specific rules, e.g. physical laws, functionality-related geometric properties, semantic cues, and so on. Thus we apply a data-driven deep learning method to automatically encode those rules and formulate the upright orientation estimation problem as a classification model, i.e. extract the points on a 3D model that forms the natural base. And then the upright orientation is computed as the normal of the natural base. Our proposed new approach has three advantages. First, it formulates the continuous orientation estimation task as a discrete classification task while preserving the continuity of the solution space. Second, it automatically learns the comprehensive criteria defining a natural base of general 3D models even with asymmetric geometry. Third, the learned orientation-aware features can serve well in downstream tasks. Results show that our approach outperforms previous approaches on orientation estimation and also achieves remarkable generalization capability and transfer capability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pang_Upright-Net_Learning_Upright_Orientation_for_3D_Point_Cloud_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Upright-Net_Learning_Upright_Orientation_for_3D_Point_Cloud_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pang_Upright-Net_Learning_Upright_Orientation_for_3D_Point_Cloud_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Semantic Associations for Mirror Detection | Huankang Guan, Jiaying Lin, Rynson W.H. Lau | Mirrors generally lack a consistent visual appearance, making mirror detection very challenging. Although recent works that are based on exploiting contextual contrasts and corresponding relations have achieved good results, heavily relying on contextual contrasts and corresponding relations to discover mirrors tend to fail in complex real-world scenes, where a lot of objects, e.g., doorways, may have similar features as mirrors. We observe that humans tend to place mirrors in relation to certain objects for specific functional purposes, e.g., a mirror above the sink. Inspired by this observation, we propose a model to exploit the semantic associations between the mirror and its surrounding objects for a reliable mirror localization. Our model first acquires class-specific knowledge of the surrounding objects via a semantic side-path. It then uses two novel modules to exploit semantic associations: 1) an Associations Exploration (AE) Module to extract the associations of the scene objects based on fully connected graph models, and 2) a Quadruple-Graph (QG) Module to facilitate the diffusion and aggregation of semantic association knowledge using graph convolutions. Extensive experiments show that our method outperforms the existing methods and sets the new state-of-the-art on both PMD dataset (f-measure: 0.844) and MSD dataset (f-measure: 0.889). | https://openaccess.thecvf.com/content/CVPR2022/papers/Guan_Learning_Semantic_Associations_for_Mirror_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Learning_Semantic_Associations_for_Mirror_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Learning_Semantic_Associations_for_Mirror_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Spatial-Temporal Parallel Transformer for Arm-Hand Dynamic Estimation | Shuying Liu, Wenbin Wu, Jiaxian Wu, Yue Lin | We propose an approach to estimate arm and hand dynamics from monocular video by utilizing the relationship between arm and hand. Although monocular full human motion capture technologies have made great progress in recent years, recovering accurate and plausible arm twists and hand gestures from in-the-wild videos still remains a challenge. To solve this problem, our solution is proposed based on the fact that arm poses and hand gestures are highly correlated in most real situations. To make best use of arm-hand correlation as well as inter-frame information, we carefully design a Spatial-Temporal Parallel Arm-Hand Motion Transformer (PAHMT) to predict the arm and hand dynamics simultaneously. We also introduce new losses to encourage the estimations to be smooth and accurate. Besides, we collect a motion capture dataset including 200K frames of hand gestures and use this data to train our model. By integrating a 2D hand pose estimation model and a 3D human pose estimation model, the proposed method can produce plausible arm and hand dynamics from monocular video. Extensive evaluations demonstrate that the proposed method has advantages over previous state-of-the-art approaches and shows robustness under various challenging scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Spatial-Temporal_Parallel_Transformer_for_Arm-Hand_Dynamic_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Spatial-Temporal_Parallel_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Spatial-Temporal_Parallel_Transformer_for_Arm-Hand_Dynamic_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Spatial-Temporal_Parallel_Transformer_for_Arm-Hand_Dynamic_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Failure Modes of Domain Generalization Algorithms | Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan | Domain generalization algorithms use training data from multiple domains to learn models that generalize well to unseen domains. While recently proposed benchmarks demonstrate that most of the existing algorithms do not outperform simple baselines, the established evaluation methods fail to expose the impact of various factors that contribute to the poor performance. In this paper we propose an evaluation framework for domain generalization algorithms that allows decomposition of the error into components capturing distinct aspects of generalization. Inspired by the prevalence of algorithms based on the idea of domain-invariant representation learning, we extend the evaluation framework to capture various types of failures in achieving invariance. We show that the largest contributor to the generalization error varies across methods, datasets, regularization strengths and even training lengths. We observe two problems associated with the strategy of learning domain-invariant representations. On Colored MNIST, most domain generalization algorithms fail because they reach domain-invariance only on the training domains. On Camelyon-17, domain-invariance degrades the quality of representations on unseen domains. We hypothesize that focusing instead on tuning the classifier on top of a rich representation can be a promising direction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Galstyan_Failure_Modes_of_Domain_Generalization_Algorithms_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Galstyan_Failure_Modes_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13733 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Galstyan_Failure_Modes_of_Domain_Generalization_Algorithms_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Galstyan_Failure_Modes_of_Domain_Generalization_Algorithms_CVPR_2022_paper.html | CVPR 2022 | null |
Geometric and Textural Augmentation for Domain Gap Reduction | Xiao-Chang Liu, Yong-Liang Yang, Peter Hall | Research has shown that convolutional neural networks for object recognition are vulnerable to changes in depiction because learning is biased towards the low-level statistics of texture patches. Recent works concentrate on improving robustness by applying style transfer to training examples to mitigate against over-fitting to one depiction style. These new approaches improve performance, but they ignore the geometric variations in object shape that real art exhibits: artists deform and warp objects for artistic effect. Motivated by this observation, we propose a method to reduce bias by jointly increasing the texture and geometry diversities of the training data. In effect, we extend the visual object class to include examples with shape changes that artists use. Specifically, we learn the distribution of warps that cover each given object class. Together with augmenting textures based on a broad distribution of styles, we show by experiments that our method improves performance on several cross-domain benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Geometric_and_Textural_Augmentation_for_Domain_Gap_Reduction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Geometric_and_Textural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Geometric_and_Textural_Augmentation_for_Domain_Gap_Reduction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Geometric_and_Textural_Augmentation_for_Domain_Gap_Reduction_CVPR_2022_paper.html | CVPR 2022 | null |
Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation | Minh Hieu Phan, The-Anh Ta, Son Lam Phung, Long Tran-Thanh, Abdesselam Bouzerdoum | Deep learning models are known to suffer from the problem of catastrophic forgetting when they incrementally learn new classes. Continual learning for semantic segmentation (CSS) is an emerging field in computer vision. We identify a problem in CSS: A model tends to be confused between old and new classes that are visually similar, which makes it forget the old ones. To address this gap, we propose REMINDER - a new CSS framework and a novel class similarity knowledge distillation (CSW-KD) method. Our CSW-KD method distills the knowledge of a previous model on old classes that are similar to the new one. This provides two main benefits: (i) selectively revising old classes that are more likely to be forgotten, and (ii) better learning new classes by relating them with the previously seen classes. Extensive experiments on Pascal-VOC 2012 and ADE20k datasets show that our approach outperforms state-of-the-art methods on standard CSS settings by up to 7.07% and 8.49%, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Phan_Class_Similarity_Weighted_Knowledge_Distillation_for_Continual_Semantic_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Phan_Class_Similarity_Weighted_Knowledge_Distillation_for_Continual_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Phan_Class_Similarity_Weighted_Knowledge_Distillation_for_Continual_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
DAD-3DHeads: A Large-Scale Dense, Accurate and Diverse Dataset for 3D Head Alignment From a Single Image | Tetiana Martyniuk, Orest Kupyn, Yana Kurlyak, Igor Krashenyi, Jiří Matas, Viktoriia Sharmanska | We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in-the-wild. It contains annotations of over 3.5K landmarks that accurately represent 3D head shape compared to the ground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset, learns shape, expression, and pose parameters, and performs 3D reconstruction of a FLAME mesh. The model also incorporates a landmark prediction branch to take advantage of rich supervision and co-training of multiple related tasks. Experimentally, DAD- 3DNet outperforms or is comparable to the state-of-the-art models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D Landmarks Estimation on DAD-3DHeads dataset. Finally, diversity of DAD-3DHeads in camera angles, facial expressions, and occlusions enables a benchmark to study in-the-wild generalization and robustness to distribution shifts. The dataset webpage is https://p.farm/research/dad-3dheads. | https://openaccess.thecvf.com/content/CVPR2022/papers/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Martyniuk_DAD-3DHeads_A_Large-Scale_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Martyniuk_DAD-3DHeads_A_Large-Scale_Dense_Accurate_and_Diverse_Dataset_for_3D_CVPR_2022_paper.html | CVPR 2022 | null |
Reconstructing Surfaces for Sparse Point Clouds With On-Surface Priors | Baorui Ma, Yu-Shen Liu, Zhizhong Han | It is an important task to reconstruct surfaces from 3D point clouds. Current methods are able to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single point clouds without ground truth signed distances or point normals. However, they require the point clouds to be dense, which dramatically limits their performance in real applications. To resolve this issue, we propose to reconstruct highly accurate surfaces from sparse point clouds with an on-surface prior. We train a neural network to learn SDFs via projecting queries onto the surface represented by the sparse point cloud. Our key idea is to infer signed distances by pushing both the query projections to be on the surface and the projection distance to be the minimum. To achieve this, we train a neural network to capture the on-surface prior to determine whether a point is on a sparse point cloud or not, and then leverage it as a differentiable function to learn SDFs from unseen sparse point cloud. Our method can learn SDFs from a single sparse point cloud without ground truth signed distances or point normals. Our numerical evaluation under widely used benchmarks demonstrates that our method achieves state-of-the-art reconstruction accuracy, especially for sparse point clouds. Code and data are available at https://github.com/mabaorui/OnSurfacePrior. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Reconstructing_Surfaces_for_Sparse_Point_Clouds_With_On-Surface_Priors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Reconstructing_Surfaces_for_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.10603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Reconstructing_Surfaces_for_Sparse_Point_Clouds_With_On-Surface_Priors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Reconstructing_Surfaces_for_Sparse_Point_Clouds_With_On-Surface_Priors_CVPR_2022_paper.html | CVPR 2022 | null |
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization | Mengtian Li, Yuan Xie, Yunhang Shen, Bo Ke, Ruizhi Qiao, Bo Ren, Shaohui Lin, Lizhuang Ma | To address the huge labeling cost in large-scale point cloud semantic segmentation, we propose a novel hybrid contrastive regularization (HybridCR) framework in weakly-supervised setting, which obtains competitive performance compared to its fully-supervised counterpart. Specifically, HybridCR is the first framework to leverage both point consistency and employ contrastive regularization with pseudo labeling in an end-to-end manner. Fundamentally, HybridCR explicitly and effectively considers the semantic similarity between local neighboring points and global characteristics of 3D classes. We further design a dynamic point cloud augmentor to generate diversity and robust sample views, whose transformation parameter is jointly optimized with model training. Through extensive experiments, HybridCR achieves significant performance improvement against the SOTA methods on both indoor and outdoor datasets, e.g., S3DIS, ScanNet-V2, Semantic3D, and SemanticKITTI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_HybridCR_Weakly-Supervised_3D_Point_Cloud_Semantic_Segmentation_via_Hybrid_Contrastive_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_HybridCR_Weakly-Supervised_3D_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_HybridCR_Weakly-Supervised_3D_Point_Cloud_Semantic_Segmentation_via_Hybrid_Contrastive_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_HybridCR_Weakly-Supervised_3D_Point_Cloud_Semantic_Segmentation_via_Hybrid_Contrastive_CVPR_2022_paper.html | CVPR 2022 | null |
Fine-Tuning Image Transformers Using Learnable Memory | Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson | In this paper we propose augmenting Vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optionally preserving its capabilities on previously learned tasks. At each layer we introduce a set of learnable embedding vectors that provide contextual information useful for specific datasets. We call these 'memory tokens'. We show that augmenting a model with just a handful of such tokens per layer significantly improves accuracy when compared to conventional head-only fine-tuning, and performs only slightly below the significantly more expensive full fine-tuning. We then propose an attention-masking approach that enables models to preserve their previous capabilities, while extending them to new downstream tasks. This approach, which we call 'non-destructive fine-tuning', enables computation reuse across multiple tasks while being able to learn new tasks independently. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sandler_Fine-Tuning_Image_Transformers_Using_Learnable_Memory_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sandler_Fine-Tuning_Image_Transformers_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sandler_Fine-Tuning_Image_Transformers_Using_Learnable_Memory_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sandler_Fine-Tuning_Image_Transformers_Using_Learnable_Memory_CVPR_2022_paper.html | CVPR 2022 | null |
Contrastive Conditional Neural Processes | Zesheng Ye, Lina Yao | Conditional Neural Processes(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-i.i.d function instantiations, CNPs are jointly optimized for in-instantiation observation prediction and cross-instantiation meta-representation adaptation within a generative reconstruction pipeline. There can be a challenge in tying together such two targets when the distribution of function observations scales to high-dimensional and noisy spaces. Instead, noise contrastive estimation might be able to provide more robust representations by learning distributional matching objectives to combat such inherent limitation of generative models. In light of this, we propose to equip CNPs by 1) aligning prediction with encoded ground-truth observation, and 2) decoupling meta-representation adaptation from generative reconstruction. Specifically, two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning (TCL) and cross-instantiation function contrastive learning (FCL), to facilitate local predictive alignment and global function consistency, respectively. We empirically show that TCL captures high-level abstraction of observations, whereas FCL helps identify underlying functions, which in turn provides more efficient representations. Our model outperforms other CNPs variants when evaluating function distribution reconstruction and parameter identification across 1D, 2D and high-dimensional time-series. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Contrastive_Conditional_Neural_Processes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Contrastive_Conditional_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03978 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Contrastive_Conditional_Neural_Processes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Contrastive_Conditional_Neural_Processes_CVPR_2022_paper.html | CVPR 2022 | null |
vCLIMB: A Novel Video Class Incremental Learning Benchmark | Andrés Villa, Kumail Alhamoud, Victor Escorcia, Fabian Caba, Juan León Alcázar, Bernard Ghanem | Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a novel video continual learning benchmark. vCLIMB is a standardized test-bed to analyze catastrophic forgetting of deep models in video continual learning. In contrast to previous work, we focus on class incremental continual learning with models trained on a sequence of disjoint tasks, and distribute the number of classes uniformly across the tasks. We perform in-depth evaluations of existing CL methods in vCLIMB, and observe two unique challenges in video data. The selection of instances to store in episodic memory is performed at the frame level. Second, untrimmed training data influences the effectiveness of frame sampling strategies. We address these two challenges by proposing a temporal consistency regularization that can be applied on top of memory-based continual learning methods. Our approach significantly improves the baseline, by up to 24% on the untrimmed continual learning task. The code of our benchmark can be found at: https://vclimb.netlify.app/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Villa_vCLIMB_A_Novel_Video_Class_Incremental_Learning_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Villa_vCLIMB_A_Novel_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.09381 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Villa_vCLIMB_A_Novel_Video_Class_Incremental_Learning_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Villa_vCLIMB_A_Novel_Video_Class_Incremental_Learning_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Bending Reality: Distortion-Aware Transformers for Adapting to Panoramic Semantic Segmentation | Jiaming Zhang, Kailun Yang, Chaoxiang Ma, Simon Reiß, Kunyu Peng, Rainer Stiefelhagen | Panoramic images with their 360deg directional view encompass exhaustive information about the surrounding space, providing a rich foundation for scene understanding. To unfold this potential in the form of robust panoramic segmentation models, large quantities of expensive, pixel-wise annotations are crucial for success. Such annotations are available, but predominantly for narrow-angle, pinhole-camera images which, off the shelf, serve as sub-optimal resources for training panoramic models. Distortions and the distinct image-feature distribution in 360deg panoramas impede the transfer from the annotation-rich pinhole domain and therefore come with a big dent in performance. To get around this domain difference and bring together semantic annotations from pinhole- and 360deg surround-visuals, we propose to learn object deformations and panoramic image distortions in the Deformable Patch Embedding (DPE) and Deformable MLP (DMLP) components which blend into our Transformer for PAnoramic Semantic Segmentation (Trans4PASS) model. Finally, we tie together shared semantics in pinhole- and panoramic feature embeddings by generating multi-scale prototype features and aligning them in our Mutual Prototypical Adaptation (MPA) for unsupervised domain adaptation. On the indoor Stanford2D3D dataset, our Trans4PASS with MPA maintains comparable performance to fully-supervised state-of-the-arts, cutting the need for over 1,400 labeled panoramas. On the outdoor DensePASS dataset, we break state-of-the-art by 14.39% mIoU and set the new bar at 56.38%. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Bending_Reality_Distortion-Aware_Transformers_for_Adapting_to_Panoramic_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Bending_Reality_Distortion-Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Bending_Reality_Distortion-Aware_Transformers_for_Adapting_to_Panoramic_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Bending_Reality_Distortion-Aware_Transformers_for_Adapting_to_Panoramic_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Sparse and Complete Latent Organization for Geospatial Semantic Segmentation | Fengyu Yang, Chenyang Ma | Geospatial semantic segmentation on remote sensing images suffers from large intra-class variance in both foreground and background classes. First, foreground objects are tiny in the remote sensing images and are represented by only a few pixels, which leads to large foreground intra-class variance and undermines the discrimination between foreground classes (issue firstly considered in this work). Second, background class contains complex context, which results in false alarms due to large background intra-class variance. To alleviate these two issues, we construct a sparse and complete latent structure via prototypes. In particular, to enhance the sparsity of the latent space, we design a prototypical contrastive learning to have prototypes of the same category clustering together and prototypes of different categories to be far away from each other. Also, we strengthen the completeness of the latent space by modeling all foreground categories and hardest (nearest) background objects. We further design a patch shuffle augmentation for remote sensing images with complicated contexts. Our augmentation encourages the semantic information of an object to be correlated only to the limited context within the patch that is specific to its category, which further reduces large intra-class variance. We conduct extensive evaluations on a large scale remote sensing dataset, showing our approach significantly outperforms state-of-the-art methods by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Sparse_and_Complete_Latent_Organization_for_Geospatial_Semantic_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Sparse_and_Complete_Latent_Organization_for_Geospatial_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Sparse_and_Complete_Latent_Organization_for_Geospatial_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Equivariant Imaging: A Fully Unsupervised Framework for Learning To Image From Noisy and Partial Measurements | Dongdong Chen, Julián Tachella, Mike E. Davies | Deep networks provide state-of-the-art performance in multiple imaging inverse problems ranging from medical imaging to computational photography. However, most existing networks are trained with clean signals which are often hard or impossible to obtain. Equivariant imaging (EI) is a recent self-supervised learning framework that exploits the group invariance present in signal distributions to learn a reconstruction function from partial measurement data alone. While EI results are impressive, its performance degrades with increasing noise. In this paper, we propose a Robust Equivariant Imaging (REI) framework which can learn to image from noisy partial measurements alone. The proposed method uses Stein's Unbiased Risk Estimator (SURE) to obtain a fully unsupervised training loss that is robust to noise. We show that REI leads to considerable performance gains on linear and nonlinear inverse problems, thereby paving the way for robust unsupervised imaging with deep networks. Code is available at https://github.com/edongdongchen/REI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Robust_Equivariant_Imaging_A_Fully_Unsupervised_Framework_for_Learning_To_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Robust_Equivariant_Imaging_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2111.12855 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Robust_Equivariant_Imaging_A_Fully_Unsupervised_Framework_for_Learning_To_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Robust_Equivariant_Imaging_A_Fully_Unsupervised_Framework_for_Learning_To_CVPR_2022_paper.html | CVPR 2022 | null |
Not All Relations Are Equal: Mining Informative Labels for Scene Graph Generation | Arushi Goel, Basura Fernando, Frank Keller, Hakan Bilen | Scene graph generation (SGG) aims to capture a wide variety of interactions between pairs of objects, which is essential for full scene understanding. Existing SGG methods trained on the entire set of relations fail to acquire complex reasoning about visual and textual correlations due to various biases in training data. Learning on trivial relations that indicate generic spatial configuration like 'on' instead of informative relations such as 'parked on' does not enforce this complex reasoning, harming generalization. To address this problem, we propose a novel framework for SGG training that exploits relation labels based on their informativeness. Our model-agnostic training procedure imputes missing informative relations for less informative samples in the training data and trains a SGG model on the imputed labels along with existing annotations. We show that this approach can successfully be used in conjunction with state-of-the-art SGG methods and improves their performance significantly in multiple metrics on the standard Visual Genome benchmark. Furthermore, we obtain considerable improvements for unseen triplets in a more challenging zero-shot setting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Goel_Not_All_Relations_Are_Equal_Mining_Informative_Labels_for_Scene_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Goel_Not_All_Relations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13517 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Goel_Not_All_Relations_Are_Equal_Mining_Informative_Labels_for_Scene_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Goel_Not_All_Relations_Are_Equal_Mining_Informative_Labels_for_Scene_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Detect Scene Landmarks for Camera Localization | Tien Do, Ondrej Miksik, Joseph DeGol, Hyun Soo Park, Sudipta N. Sinha | Modern camera localization methods that use image retrieval, feature matching, and 3D structure-based pose estimation require long-term storage of numerous scene images or a vast amount of image features. This can make them unsuitable for resource constrained VR/AR devices and also raises serious privacy concerns. We present a new learned camera localization technique that eliminates the need to store features or a detailed 3D point cloud. Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible. We refer to these points as scene landmarks. We also show that a CNN can be trained to regress bearing vectors for such landmarks even when they are not within the camera's field-of-view. We demonstrate that the predicted landmarks yield accurate pose estimates and that our method outperforms DSAC*, the state-of-the-art in learned localization. Furthermore, extending HLoc (an accurate method) by combining its correspondences with our predictions, boosts its accuracy even further. | https://openaccess.thecvf.com/content/CVPR2022/papers/Do_Learning_To_Detect_Scene_Landmarks_for_Camera_Localization_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Do_Learning_To_Detect_Scene_Landmarks_for_Camera_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Do_Learning_To_Detect_Scene_Landmarks_for_Camera_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation | Leyao Liu, Tian Zheng, Yun-Jou Lin, Kai Ni, Lu Fang | We propose INS-Conv, an INcremental Sparse Convolutional network which enables online accurate 3D semantic and instance segmentation. Benefiting from the incremental nature of RGB-D reconstruction, we only need to update the residuals between the reconstructed scenes of consecutive frames, which are usually sparse. For layer design, we define novel residual propagation rules for sparse convolution operations, achieving close approximation to standard sparse convolution. For network architecture, an uncertainty term is proposed to adaptively select which residual to update, further improving the inference accuracy and efficiency. Based on INS-Conv, an online joint 3D semantic and instance segmentation pipeline is proposed, reaching an inference speed of 15 FPS on GPU and 10 FPS on CPU. Experiments on ScanNetv2 and SceneNN datasets show that the accuracy of our method surpasses previous online methods by a large margin, and is on par with state-of-the-art offline methods. A live demo on portable devices further shows the superior performance of INS-Conv. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_INS-Conv_Incremental_Sparse_Convolution_for_Online_3D_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_INS-Conv_Incremental_Sparse_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_INS-Conv_Incremental_Sparse_Convolution_for_Online_3D_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_INS-Conv_Incremental_Sparse_Convolution_for_Online_3D_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
ST++: Make Self-Training Work Better for Semi-Supervised Semantic Segmentation | Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao | Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student. With this simple mechanism, our ST outperforms all existing methods without any bells and whistles, e.g., iterative re-training. Inspired by the impressive results, we thoroughly investigate the SDA and provide some empirical analysis. Nevertheless, incorrect pseudo labels are still prone to accumulate and degrade the performance. To this end, we further propose an advanced self-training framework (namely ST++), that performs selective re-training via prioritizing reliable unlabeled images based on holistic prediction-level stability. Concretely, several model checkpoints are saved in the first stage supervised training, and the discrepancy of their predictions on the unlabeled image serves as a measurement for reliability. Our image-level selection offers holistic contextual information for learning. We demonstrate that it is more suitable for segmentation than common pixel-wise selection. As a result, ST++ further boosts the performance of our ST. Code is available at https://github.com/LiheYoung/ST-PlusPlus. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_ST_Make_Self-Training_Work_Better_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_ST_Make_Self-Training_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_ST_Make_Self-Training_Work_Better_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_ST_Make_Self-Training_Work_Better_for_Semi-Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Visual Vibration Tomography: Estimating Interior Material Properties From Monocular Video | Berthy T. Feng, Alexander C. Ogren, Chiara Daraio, Katherine L. Bouman | An object's interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations. Specifically, we show how to estimate Young's modulus and density throughout a 3D object with known geometry. Knowledge of how these values change across the object is useful for simulating its motion and characterizing any defects. Traditional non-destructive testing approaches, which often require expensive instruments, generally estimate only homogenized material properties or simply identify the presence of defects. In contrast, our approach leverages monocular video to (1) identify image-space modes from an object's sub-pixel motion, and (2) directly infer spatially-varying Young's modulus and density values from the observed modes. We demonstrate our approach on both simulated and real videos. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Visual_Vibration_Tomography_Estimating_Interior_Material_Properties_From_Monocular_Video_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Feng_Visual_Vibration_Tomography_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.02735 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Visual_Vibration_Tomography_Estimating_Interior_Material_Properties_From_Monocular_Video_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Visual_Vibration_Tomography_Estimating_Interior_Material_Properties_From_Monocular_Video_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Global-Local Structure Modeling for Point Cloud Domain Adaptation With Reliable Voted Pseudo Labels | Hehe Fan, Xiaojun Chang, Wanyue Zhang, Yi Cheng, Ying Sun, Mohan Kankanhalli | In this paper, we propose an unsupervised domain adaptation method for deep point cloud representation learning. To model the internal structures in target point clouds, we first propose to learn the global representations of unlabeled data by scaling up or down point clouds and then predicting the scales. Second, to capture the local structure in a self-supervised manner, we propose to project a 3D local area onto a 2D plane and then learn to reconstruct the squeezed region. Moreover, to effectively transfer the knowledge from source domain, we propose to vote pseudo labels for target samples based on the labels of their nearest source neighbors in the shared feature space. To avoid the noise caused by incorrect pseudo labels, we only select reliable target samples, whose voting consistencies are high enough, for enhancing adaptation. The voting method is able to adaptively select more and more target samples during training, which in return facilitates adaptation because the amount of labeled target data increases. Experiments on PointDA (ModelNet-10, ShapeNet-10 and ScanNet-10) and Sim-to-Real (ModelNet-11, ScanObjectNN-11, ShapeNet-9 and ScanObjectNN-9) demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_Self-Supervised_Global-Local_Structure_Modeling_for_Point_Cloud_Domain_Adaptation_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_Self-Supervised_Global-Local_Structure_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Self-Supervised_Global-Local_Structure_Modeling_for_Point_Cloud_Domain_Adaptation_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_Self-Supervised_Global-Local_Structure_Modeling_for_Point_Cloud_Domain_Adaptation_With_CVPR_2022_paper.html | CVPR 2022 | null |
Interacting Attention Graph for Single Image Two-Hand Reconstruction | Mengcheng Li, Liang An, Hongwen Zhang, Lianpeng Wu, Feng Chen, Tao Yu, Yebin Liu | Graph convolutional network (GCN) has achieved great success in single hand reconstruction task, while interacting two-hand reconstruction by GCN remains unexplored. In this paper, we present Interacting Attention Graph Hand (IntagHand), the first graph convolution based network that reconstructs two interacting hands from a single RGB image. To solve occlusion and interaction challenges of two-hand reconstruction, we introduce two novel attention based modules in each upsampling step of the original GCN. The first module is the pyramid image feature attention (PIFA) module, which utilizes multiresolution features to implicitly obtain vertex-to-image alignment. The second module is the cross hand attention (CHA) module that encodes the coherence of interacting hands by building dense cross-attention between two hand vertices. As a result, our model outperforms all existing two-hand reconstruction methods by a large margin on InterHand2.6M benchmark. Moreover, ablation studies verify the effectiveness of both PIFA and CHA modules for improving the reconstruction accuracy. Results on in-the-wild images and live video streams further demonstrate the generalization ability of our network. Our code is available at https://github.com/Dw1010/IntagHand. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Interacting_Attention_Graph_for_Single_Image_Two-Hand_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Interacting_Attention_Graph_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09364 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Interacting_Attention_Graph_for_Single_Image_Two-Hand_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Interacting_Attention_Graph_for_Single_Image_Two-Hand_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task | Xiaoqing Ye, Mao Shu, Hanyu Li, Yifeng Shi, Yingying Li, Guangjie Wang, Xiao Tan, Errui Ding | Concurrent perception datasets for autonomous driving are mainly limited to frontal view with sensors mounted on the vehicle. None of them is designed for the overlooked roadside perception tasks. On the other hand, the data captured from roadside cameras have strengths over frontal-view data, which is believed to facilitate a safer and more intelligent autonomous driving system. To accelerate the progress of roadside perception, we present the first high-diversity challenging Roadside Perception 3D dataset- Rope3D from a novel view. The dataset consists of 50k images and over 1.5M 3D objects in various scenes, which are captured under different settings including various cameras with ambiguous mounting positions, camera specifications, viewpoints, and different environmental conditions. We conduct strict 2D-3D joint annotation and comprehensive data analysis, as well as set up a new 3D roadside perception benchmark with metrics and evaluation devkit. Furthermore, we tailor the existing frontal-view monocular 3D object detection approaches and propose to leverage the geometry constraint to solve the inherent ambiguities caused by various sensors, viewpoints. Our dataset is available on https://thudair.baai.ac.cn/rope. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Rope3D_The_Roadside_Perception_Dataset_for_Autonomous_Driving_and_Monocular_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Rope3D_The_Roadside_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Rope3D_The_Roadside_Perception_Dataset_for_Autonomous_Driving_and_Monocular_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Rope3D_The_Roadside_Perception_Dataset_for_Autonomous_Driving_and_Monocular_CVPR_2022_paper.html | CVPR 2022 | null |
Noisy Boundaries: Lemon or Lemonade for Semi-Supervised Instance Segmentation? | Zhenyu Wang, Yali Li, Shengjin Wang | Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the performance. In this paper, we formally address semi-supervised instance segmentation, where unlabeled images are employed to boost the performance. We construct a framework for semi-supervised instance segmentation by assigning pixel-level pseudo labels. Under this framework, we point out that noisy boundaries associated with pseudo labels are double-edged. We propose to exploit and resist them in a unified manner simultaneously: 1) To combat the negative effects of noisy boundaries, we propose a noise-tolerant mask head by leveraging low-resolution features. 2) To enhance the positive impacts, we introduce a boundary-preserving map for learning detailed information within boundary-relevant regions. We evaluate our approach by extensive experiments. It behaves extraordinarily, outperforming the supervised baseline by a large margin, more than 6% on Cityscapes, 7% on COCO and 4.5% on BDD100k. On Cityscapes, our method achieves comparable performance by utilizing only 30% labeled images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Noisy_Boundaries_Lemon_or_Lemonade_for_Semi-Supervised_Instance_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Noisy_Boundaries_Lemon_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Noisy_Boundaries_Lemon_or_Lemonade_for_Semi-Supervised_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Noisy_Boundaries_Lemon_or_Lemonade_for_Semi-Supervised_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Boosting View Synthesis With Residual Transfer | Xuejian Rong, Jia-Bin Huang, Ayush Saraf, Changil Kim, Johannes Kopf | Volumetric view synthesis methods with neural representations, such as NeRF and NeX, have recently demonstrated high-quality novel view synthesis. Optimizing these representations is slow, however, and even fully trained models cannot reproduce all fine details in the input views. We present a simple but effective technique to boost the rendering quality, which can be easily integrated with most view synthesis methods. The core idea is to adaptively transfer color residuals (the difference between the input images and their reconstruction) from training views to novel views. We blend the residuals from multiple views using a heuristic weighting scheme depending on ray visibility and angular differences. We integrate our technique with several state-of-the-art view synthesis methods and evaluate on the Real Forward-facing and the Shiny datasets. Our results show that at about 1/10th the number of training iterations we achieve the same rendering quality as fully converged NeRF and NeX models, and when applied to fully converged models we significantly improve their rendering quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rong_Boosting_View_Synthesis_With_Residual_Transfer_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rong_Boosting_View_Synthesis_With_Residual_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rong_Boosting_View_Synthesis_With_Residual_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
Input-Level Inductive Biases for 3D Reconstruction | Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, Andrew Zisserman | Much of the recent progress in 3D vision has been driven by the development of specialized architectures that incorporate geometrical inductive biases. In this paper we tackle 3D reconstruction using a domain agnostic architecture and study how instead to inject the same type of inductive biases directly as extra inputs to the model. This approach makes it possible to apply existing general models, such as Perceivers, on this rich domain, without the need for architectural changes, while simultaneously maintaining data efficiency of bespoke models. In particular we study how to encode cameras, projective ray incidence and epipolar geometry as model inputs, and demonstrate competitive multi-view depth estimation performance on multiple benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yifan_Input-Level_Inductive_Biases_for_3D_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yifan_Input-Level_Inductive_Biases_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yifan_Input-Level_Inductive_Biases_for_3D_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yifan_Input-Level_Inductive_Biases_for_3D_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring and Evaluating Image Restoration Potential in Dynamic Scenes | Cheng Zhang, Shaolin Su, Yu Zhu, Qingsen Yan, Jinqiu Sun, Yanning Zhang | In dynamic scenes, images often suffer from dynamic blur due to superposition of motions or low signal-noise ratio resulted from quick shutter speed when avoiding motions. Recovering sharp and clean result from the captured images heavily depends on the ability of restoration methods and the quality of the input. Though existing research on image restoration focuses on developing models for obtaining better restored results, less have studied to evaluate how and which input image leads to superior restored quality. In this paper, to better study an image's potential value that can be explored for restoration, we propose a novel concept, referring to image restoration potential (IRP). Specifically, We first establish a dynamic scene imaging dataset containing composite distortions and applied image restoration processes to validate the rationality of the existence to IRP. Based on this dataset, we investigate into several properties of IRP and propose a novel deep model to accurately predict IRP values. By gradually distilling and selective fusing the degradation features, the proposed model shows its superiority in IRP prediction. Thanks to the proposed model, we are then able to validate how various image restoration related applications are benefited from IRP prediction. We show the potential usages of IRP as a filtering principle to select valuable frames, an auxiliary guidance to improve restoration models, and also an indicator to optimize camera settings for capturing better images under dynamic scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Exploring_and_Evaluating_Image_Restoration_Potential_in_Dynamic_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Exploring_and_Evaluating_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11754 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_and_Evaluating_Image_Restoration_Potential_in_Dynamic_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Exploring_and_Evaluating_Image_Restoration_Potential_in_Dynamic_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
FashionVLP: Vision Language Transformer for Fashion Retrieval With Feedback | Sonam Goenka, Zhaoheng Zheng, Ayush Jaiswal, Rakesh Chada, Yue Wu, Varsha Hedau, Pradeep Natarajan | Fashion image retrieval based on a query pair of reference image and natural language feedback is a challenging task that requires models to assess fashion related information from visual and textual modalities simultaneously. We propose a new vision-language transformer based model, FashionVLP, that brings the prior knowledge contained in large image-text corpora to the domain of fashion image re-trieval, and combines visual information from multiple levels of context to effectively capture fashion related information. While queries are encoded through the transformer layers, our asymmetric design adopts a novel attention-based approach for fusing target image features without involving text or transformer layers in the process. Extensive results show that FashionVLP achieves the state-of-the-art performance on benchmark datasets, with a large 23% relative improvement on the challenging FashionIQ dataset, which contains complex natural language feedback. | https://openaccess.thecvf.com/content/CVPR2022/papers/Goenka_FashionVLP_Vision_Language_Transformer_for_Fashion_Retrieval_With_Feedback_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Goenka_FashionVLP_Vision_Language_Transformer_for_Fashion_Retrieval_With_Feedback_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Goenka_FashionVLP_Vision_Language_Transformer_for_Fashion_Retrieval_With_Feedback_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Image Relational Knowledge Distillation for Semantic Segmentation | Chuanguang Yang, Helong Zhou, Zhulin An, Xue Jiang, Yongjun Xu, Qian Zhang | Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples. However, they ignore the global semantic relations among pixels across various images that are valuable for KD. This paper proposes a novel Cross-Image Relational KD (CIRKD), which focuses on transferring structured pixel-to-pixel and pixel-to-region relations among the whole images. The motivation is that a good teacher network could construct a well-structured feature space in terms of global pixel dependencies. CIRKD makes the student mimic better structured semantic relations from the teacher, thus improving the segmentation performance. Experimental results over Cityscapes, CamVid and Pascal VOC datasets demonstrate the effectiveness of our proposed approach against state-of-the-art distillation methods. The code is available at https://github.com/winycg/CIRKD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Cross-Image_Relational_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Cross-Image_Relational_Knowledge_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06986 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Cross-Image_Relational_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Cross-Image_Relational_Knowledge_Distillation_for_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
A-ViT: Adaptive Tokens for Efficient Vision Transformer | Hongxu Yin, Arash Vahdat, Jose M. Alvarez, Arun Mallya, Jan Kautz, Pavlo Molchanov | We introduce A-ViT, a method that adaptively adjusts the inference cost of vision transformer ViT for images of different complexity. A-ViT achieves this by automatically reducing the number of tokens in vision transformers that are processed in the network as inference proceeds. We reformulate Adaptive Computation Time (ACT) for this task, extending halting to discard redundant spatial tokens. The appealing architectural properties of vision transformers enables our adaptive token reduction mechanism to speed up inference without modifying the network architecture or inference hardware. We demonstrate that A-ViT requires no extra parameters or sub-network for halting, as we base the learning of adaptive halting on the original network parameters. We further introduce distributional prior regularization that stabilizes training compared to prior ACT approaches. On the image classification task (ImageNet1K), we show that our proposed A-ViT yields high efficacy in filtering informative spatial features and cutting down on the overall compute. The proposed method improves the throughput of DeiT-Tiny by 62% and DeiT-Small by 38% with only 0.3% accuracy drop, outperforming prior art by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yin_A-ViT_Adaptive_Tokens_for_Efficient_Vision_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yin_A-ViT_Adaptive_Tokens_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_A-ViT_Adaptive_Tokens_for_Efficient_Vision_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yin_A-ViT_Adaptive_Tokens_for_Efficient_Vision_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Think Global, Act Local: Dual-Scale Graph Transformer for Vision-and-Language Navigation | Shizhe Chen, Pierre-Louis Guhur, Makarand Tapaswi, Cordelia Schmid, Ivan Laptev | Following language instructions to navigate in unseen environments is a challenging problem for autonomous embodied agents. The agent not only needs to ground languages in visual scenes, but also should explore the environment to reach its target. In this work, we propose a dual-scale graph transformer (DUET) for joint long-term action planning and fine-grained cross-modal understanding. We build a topological map on-the-fly to enable efficient exploration in global action space. To balance the complexity of large action space reasoning and fine-grained language grounding, we dynamically combine a fine-scale encoding over local observations and a coarse-scale encoding on a global map via graph transformers. The proposed approach, DUET, significantly outperforms state-of-the-art methods on goal-oriented vision-and-language navigation (VLN) benchmarks REVERIE and SOON. It also improves the success rate on the fine-grained VLN benchmark R2R. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Think_Global_Act_Local_Dual-Scale_Graph_Transformer_for_Vision-and-Language_Navigation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Think_Global_Act_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.11742 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Think_Global_Act_Local_Dual-Scale_Graph_Transformer_for_Vision-and-Language_Navigation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Think_Global_Act_Local_Dual-Scale_Graph_Transformer_for_Vision-and-Language_Navigation_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Layer-Wise Image Vectorization | Xu Ma, Yuqian Zhou, Xingqian Xu, Bin Sun, Valerii Filev, Nikita Orlov, Yun Fu, Humphrey Shi | Image rasterization is a mature technique in computer graphics, while image vectorization, the reverse path of rasterization, remains a major challenge. Recent ad- vanced deep learning-based models achieve vectorization and semantic interpolation of vector graphs and demon- strate a better topology of generating new figures. How- ever, deep models cannot be easily generalized to out-of- domain testing data. The generated SVGs also contain complex and redundant shapes that are not quite conve- nient for further editing. Specifically, the crucial layer- wise topology and fundamental semantics in images are still not well understood and thus not fully explored. In this work, we propose Layer-wise Image Vectorization, namely LIVE, to convert raster images to SVGs and simultaneously maintain its image topology. LIVE can generate compact SVG forms with layer-wise structures that are semantically consistent with the human perspective. We progressively add new bezier paths and optimize these paths with the layer-wise framework, newly designed loss functions, and component-wise path initialization technique. Our experi- ments demonstrate that LIVE presents more plausible vec- torized forms than prior works and can be generalized to new images. With the help of this newly learned topol- ogy, LIVE initiates human editable SVGs for both design- ers and other downstream applications. Codes are made available at https://github.com/Picsart-AI-Research/LIVE- Layerwise-Image-Vectorization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Towards_Layer-Wise_Image_Vectorization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Towards_Layer-Wise_Image_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Towards_Layer-Wise_Image_Vectorization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Towards_Layer-Wise_Image_Vectorization_CVPR_2022_paper.html | CVPR 2022 | null |
Scenic: A JAX Library for Computer Vision Research and Beyond | Mostafa Dehghani, Alexey Gritsenko, Anurag Arnab, Matthias Minderer, Yi Tay | Scenic is an open-source (https://github.com/google-research/scenic) JAX library with a focus on transformer-based models for computer vision research and beyond. The goal of this toolkit is to facilitate rapid experimentation, prototyping, and research of new architectures and models. Scenic supports a diverse range of tasks (e.g., classification, segmentation, detection) and facilitates working on multi-modal problems, along with GPU/TPU support for large-scale, multi-host and multi-device training. Scenic also offers optimized implementations of state-of-the-art research models spanning a wide range of modalities. Scenic has been successfully used for numerous projects and published papers and continues serving as the library of choice for rapid prototyping and publication of new research ideas. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dehghani_Scenic_A_JAX_Library_for_Computer_Vision_Research_and_Beyond_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2110.11403 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dehghani_Scenic_A_JAX_Library_for_Computer_Vision_Research_and_Beyond_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dehghani_Scenic_A_JAX_Library_for_Computer_Vision_Research_and_Beyond_CVPR_2022_paper.html | CVPR 2022 | null |
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters | Paul Gavrikov, Janis Keuper | Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gavrikov_CNN_Filter_DB_An_Empirical_Investigation_of_Trained_Convolutional_Filters_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gavrikov_CNN_Filter_DB_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gavrikov_CNN_Filter_DB_An_Empirical_Investigation_of_Trained_Convolutional_Filters_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gavrikov_CNN_Filter_DB_An_Empirical_Investigation_of_Trained_Convolutional_Filters_CVPR_2022_paper.html | CVPR 2022 | null |
ScePT: Scene-Consistent, Policy-Based Trajectory Predictions for Planning | Yuxiao Chen, Boris Ivanovic, Marco Pavone | Trajectory prediction is a critical functionality of autonomous systems that share environments with uncontrolled agents, one prominent example being self-driving vehicles. Currently, most prediction methods do not enforce scene consistency, i.e., there are a substantial amount of self-collisions between predicted trajectories of different agents in the scene. Moreover, many approaches generate individual trajectory predictions per agent instead of joint trajectory predictions of the whole scene, which makes downstream planning difficult. In this work, we present ScePT, a policy planning-based trajectory prediction model that generates accurate, scene-consistent trajectory predictions suitable for autonomous system motion planning. It explicitly enforces scene consistency and learns an agent interaction policy that can be used for conditional prediction. Experiments on multiple real-world pedestrians and autonomous vehicle datasets show that ScePT matches current state-of-the-art prediction accuracy with significantly improved scene consistency. We also demonstrate ScePT's ability to work with a downstream contingency planner. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_ScePT_Scene-Consistent_Policy-Based_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_ScePT_Scene-Consistent_Policy-Based_Trajectory_Predictions_for_Planning_CVPR_2022_paper.html | CVPR 2022 | null |
Calibrating Deep Neural Networks by Pairwise Constraints | Jiacheng Cheng, Nuno Vasconcelos | It is well known that deep neural networks (DNNs) produce poorly calibrated estimates of class-posterior probabilities. We hypothesize that this is due to the limited calibration supervision provided by the cross-entropy loss, which places all emphasis on the probability of the true class and mostly ignores the remaining. We consider how each example can supervise all classes and show that the calibration of a C-way classification problem is equivalent to the calibration of C(C-1)/2 pairwise binary classification problems that can be derived from it. This suggests the hypothesis that DNN calibration can be improved by providing calibration supervision to all such binary problems. An implementation of this calibration by pairwise constraints (CPC) is then proposed, based on two types of binary calibration constraints. This is finally shown to be implementable with a very minimal increase in the complexity of cross-entropy training. Empirical evaluations of the proposed CPC method across multiple datasets and DNN architectures demonstrate state-of-the-art calibration performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Calibrating_Deep_Neural_Networks_by_Pairwise_Constraints_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cheng_Calibrating_Deep_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Calibrating_Deep_Neural_Networks_by_Pairwise_Constraints_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Calibrating_Deep_Neural_Networks_by_Pairwise_Constraints_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Saliency Prior for Reducing Visual Distraction | Kfir Aberman, Junfeng He, Yossi Gandelsman, Inbar Mosseri, David E. Jacobs, Kai Kohlhoff, Yael Pritch, Michael Rubinstein | Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distraction in images. Given an image and a mask specifying the region to edit, we backpropagate through a state-of-the-art saliency model to parameterize a differentiable editing operator, such that the saliency within the masked region is reduced. We demonstrate several operators, including: a recoloring operator, which learns to apply a color transform that camouflages and blends distractors into their surroundings; a warping operator, which warps less salient image regions to cover distractors, gradually collapsing objects into themselves and effectively removing them (an effect akin to inpainting); a GAN operator, which uses a semantic prior to fully replace image regions with plausible, less salient alternatives. The resulting effects are consistent with cognitive research on the human visual system (e.g., since color mismatch is salient, the recoloring operator learns to harmonize objects' colors with their surrounding to reduce their saliency), and, importantly, are all achieved solely through the guidance of the pretrained saliency model. We present results on a variety of natural images and conduct a perceptual study to evaluate and validate the changes in viewers' eye-gaze between the original images and our edited results. | https://openaccess.thecvf.com/content/CVPR2022/papers/Aberman_Deep_Saliency_Prior_for_Reducing_Visual_Distraction_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2109.01980 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aberman_Deep_Saliency_Prior_for_Reducing_Visual_Distraction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aberman_Deep_Saliency_Prior_for_Reducing_Visual_Distraction_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Large-Scale Localization by Global Instance Recognition | Fei Xue, Ignas Budvytis, Daniel Olmeda Reino, Roberto Cipolla | Hierarchical frameworks consisting of both coarse and fine localization are often used as the standard pipeline for large-scale visual localization. Despite their promising performance in simple environments, they still suffer from low efficiency and accuracy in large-scale scenes, especially under challenging conditions. In this paper, we propose an efficient and accurate large-scale localization framework based on the recognition of buildings, which are not only discriminative for coarse localization but also robust for fine localization. Specifically, we assign each building instance a global ID and perform pixel-wise recognition of these global instances in the localization process. For coarse localization, we employ an efficient reference search strategy to find candidates progressively from the local map observing recognized instances instead of the whole database. For fine localization, predicted labels are further used for instance-wise feature detection and matching, allowing our model to focus on fewer but more robust keypoints for establishing correspondences. The experiments in long-term large-scale localization datasets including Aachen and RobotCar-Seasons demonstrate that our method outperforms previous approaches consistently in terms of both efficiency and accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Efficient_Large-Scale_Localization_by_Global_Instance_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xue_Efficient_Large-Scale_Localization_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Efficient_Large-Scale_Localization_by_Global_Instance_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Efficient_Large-Scale_Localization_by_Global_Instance_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Sign Language Video Retrieval With Free-Form Textual Queries | Amanda Duarte, Samuel Albanie, Xavier Giró-i-Nieto, Gül Varol | Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with free-form textual queries: given a written query (e.g. a sentence) and a large collection of sign language videos, the objective is to find the signing video that best matches the written query. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labelled training data. We, therefore, propose SPOT-ALIGN, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data. We validate the effectiveness of SPOT-ALIGN for learning a robust sign video embedding through improvements in both sign recognition and the proposed video retrieval task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Duarte_Sign_Language_Video_Retrieval_With_Free-Form_Textual_Queries_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Duarte_Sign_Language_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Duarte_Sign_Language_Video_Retrieval_With_Free-Form_Textual_Queries_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Duarte_Sign_Language_Video_Retrieval_With_Free-Form_Textual_Queries_CVPR_2022_paper.html | CVPR 2022 | null |
Real-Time Object Detection for Streaming Perception | Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Jian Sun | Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment after processing, streaming perception is proposed to jointly evaluate the latency and accuracy into a single metric for video online perception. In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem. We build a simple and effective framework for streaming perception. It equips a novel DualFlow Perception module (DFP), which includes dynamic and static flows to capture the moving trend and basic detection feature for streaming prediction. Further, we introduce a Trend-Aware Loss (TAL) combined with a trend factor to generate adaptive weights for objects with different moving speeds. Our simple method achieves competitive performance on Argoverse-HD dataset and improves the AP by 4.9% compared to the strong baseline, validating its effectiveness. Our code will be made available at https://github.com/yancie-yjr/StreamYOLO. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Real-Time_Object_Detection_for_Streaming_Perception_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.12338 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Real-Time_Object_Detection_for_Streaming_Perception_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Real-Time_Object_Detection_for_Streaming_Perception_CVPR_2022_paper.html | CVPR 2022 | null |
Simulated Adversarial Testing of Face Recognition Models | Nataniel Ruiz, Adam Kortylewski, Weichao Qiu, Cihang Xie, Sarah Adel Bargal, Alan Yuille, Stan Sclaroff | Most machine learning models are validated and tested on fixed datasets. This can give an incomplete picture of the capabilities and weaknesses of the model. Such weaknesses can be revealed at test time in the real world. The risks involved in such failures can be loss of profits, loss of time or even loss of life in certain critical applications. In order to alleviate this issue, simulators can be controlled in a fine-grained manner using interpretable parameters to explore the semantic image manifold. In this work, we propose a framework for learning how to test machine learning algorithms using simulators in an adversarial manner in order to find weaknesses in the model before deploying it in critical scenarios. We apply this method in a face recognition setup. We show that certain weaknesses of models trained on real data can be discovered using simulated samples. Using our proposed method, we can find adversarial synthetic faces that fool contemporary face recognition models. This demonstrates the fact that these models have weaknesses that are not measured by commonly used validation datasets. We hypothesize that this type of adversarial examples are not isolated, but usually lie in connected spaces in the latent space of the simulator. We present a method to find these adversarial regions as opposed to the typical adversarial points found in the adversarial example literature. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ruiz_Simulated_Adversarial_Testing_of_Face_Recognition_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ruiz_Simulated_Adversarial_Testing_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.04569 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ruiz_Simulated_Adversarial_Testing_of_Face_Recognition_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ruiz_Simulated_Adversarial_Testing_of_Face_Recognition_Models_CVPR_2022_paper.html | CVPR 2022 | https://openaccess.thecvf.com |
VisualHow: Multimodal Problem Solving | Jinhui Yang, Xianyu Chen, Ming Jiang, Shi Chen, Louis Wang, Qi Zhao | Recent progress in the interdisciplinary studies of computer vision (CV) and natural language processing (NLP) has enabled the development of intelligent systems that can describe what they see and answer questions accordingly. However, despite showing usefulness in performing these vision-language tasks, existing methods still struggle in understanding real-life problems (i.e., how to do something) and suggesting step-by-step guidance to solve them. With an overarching goal of developing intelligent systems to assist humans in various daily activities, we propose VisualHow, a free-form and open-ended research that focuses on understanding a real-life problem and deriving its solution by incorporating key components across multiple modalities. We develop a new dataset with 20,028 real-life problems and 102,933 steps that constitute their solutions, where each step consists of both a visual illustration and a textual description that guide the problem solving. To establish better understanding of problems and solutions, we also provide annotations of multimodal attention that localizes important components across modalities and solution graphs that encapsulate different steps in structured representations. These data and annotations enable a family of new vision-language tasks that solve real-life problems. Through extensive experiments with representative models, we demonstrate their effectiveness on training and testing models for the new tasks, and there is significant scope for improvement by learning effective attention mechanisms. Our dataset and models are available at https://github.com/formidify/VisualHow. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_VisualHow_Multimodal_Problem_Solving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_VisualHow_Multimodal_Problem_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_VisualHow_Multimodal_Problem_Solving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_VisualHow_Multimodal_Problem_Solving_CVPR_2022_paper.html | CVPR 2022 | null |
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets | Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh | Pooling multiple neuroimaging datasets across institutions often enables significant improvements in statistical power when evaluating associations (e.g., between risk factors and disease outcomes) that would otherwise be too weak to detect. When there is only a single source of variability (e.g., different scanners), domain adaptation and matching the distributions of representations may suffice in many scenarios. But in the presence of more than one nuisance variable which concurrently influence the measurements, pooling datasets poses unique challenges, e.g., variations in the data can come from both the acquisition method as well as the demographics of participants (gender, age). Invariant representation learning, by itself, is ill-suited to fully model the data generation process. In this paper, we show how bringing recent results on equivariant representation learning (for studying symmetries in neural networks) together with simple use of classical results on causal inference provides an effective practical solution to this problem. In particular, we demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable (relatively) painless analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lokhande_Equivariance_Allows_Handling_Multiple_Nuisance_Variables_When_Analyzing_Pooled_Neuroimaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lokhande_Equivariance_Allows_Handling_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15234 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lokhande_Equivariance_Allows_Handling_Multiple_Nuisance_Variables_When_Analyzing_Pooled_Neuroimaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lokhande_Equivariance_Allows_Handling_Multiple_Nuisance_Variables_When_Analyzing_Pooled_Neuroimaging_CVPR_2022_paper.html | CVPR 2022 | null |
Spatial Commonsense Graph for Object Localisation in Partial Scenes | Francesco Giuliari, Geri Skenderi, Marco Cristani, Yiming Wang, Alessio Del Bue | We solve object localisation in partial scenes, a new problem of estimating the unknown position of an object (e.g. where is the bag?) given a partial 3D scan of a scene. The proposed solution is based on a novel scene graph model, the Spatial Commonsense Graph (SCG), where objects are the nodes and edges define pairwise distances between them, enriched by concept nodes and relationships from a commonsense knowledge base. This allows SCG to better generalise its spatial inference to unknown 3D scenes. The SCG is used to estimate the unknown position of the target object in two steps: first, we feed the SCG into a novel Proximity Prediction Network, a graph neural network that uses attention to perform distance prediction between the node representing the target object and the nodes representing the observed objects in the SCG; second, we propose a Localisation Module based on circular intersection to estimate the object position using all the predicted pairwise distances in order to be independent of any reference system. We create a new dataset of partially reconstructed scenes to benchmark our method and baselines for object localisation in partial scenes, where our proposed method achieves the best localisation performance. Code and Dataset are available here: https://github.com/IIT-PAVIS/SpatialCommonsenseGraph | https://openaccess.thecvf.com/content/CVPR2022/papers/Giuliari_Spatial_Commonsense_Graph_for_Object_Localisation_in_Partial_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Giuliari_Spatial_Commonsense_Graph_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05380 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Giuliari_Spatial_Commonsense_Graph_for_Object_Localisation_in_Partial_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Giuliari_Spatial_Commonsense_Graph_for_Object_Localisation_in_Partial_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
CAT-Det: Contrastively Augmented Transformer for Multi-Modal 3D Object Detection | Yanan Zhang, Jiaxin Chen, Di Huang | In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities with complementary cues for 3D object detection. However, it is quite difficult to sufficiently use them, due to large inter-modal discrepancies. To address this issue, we propose a novel framework, namely Contrastively Augmented Transformer for multi-modal 3D object Detection (CAT-Det). Specifically, CAT-Det adopts a two-stream structure consisting of a Pointformer (PT) branch, an Imageformer (IT) branch along with a Cross-Modal Transformer (CMT) module. PT, IT and CMT jointly encode intra-modal and inter-modal long-range contexts for representing an object, thus fully exploring multi-modal information for detection. Furthermore, we propose an effective One-way Multi-modal Data Augmentation (OMDA) approach via hierarchical contrastive learning at both the point and object levels, significantly improving the accuracy only by augmenting point-clouds, which is free from complex generation of paired samples of the two modalities. Extensive experiments on the KITTI benchmark show that CAT-Det achieves a new state-of-the-art, highlighting its effectiveness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_CAT-Det_Contrastively_Augmented_Transformer_for_Multi-Modal_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_CAT-Det_Contrastively_Augmented_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_CAT-Det_Contrastively_Augmented_Transformer_for_Multi-Modal_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_CAT-Det_Contrastively_Augmented_Transformer_for_Multi-Modal_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
OSSGAN: Open-Set Semi-Supervised Image Generation | Kai Katsumata, Duc Minh Vo, Hideki Nakayama | We introduce a challenging training scheme of conditional GANs, called open-set semi-supervised image generation, where the training dataset consists of two parts: (i) labeled data and (ii) unlabeled data with samples belonging to one of the labeled data classes, namely, a closed-set, and samples not belonging to any of the labeled data classes, namely, an open-set. Unlike the existing semi-supervised image generation task, where unlabeled data only contain closed-set samples, our task is more general and lowers the data collection cost in practice by allowing open-set samples to appear. Thanks to entropy regularization, the classifier that is trained on labeled data is able to quantify sample-wise importance to the training of cGAN as confidence, allowing us to use all samples in unlabeled data. We design OSSGAN, which provides decision clues to the discriminator on the basis of whether an unlabeled image belongs to one or none of the classes of interest, smoothly integrating labeled and unlabeled data during training. The results of experiments on Tiny ImageNet and ImageNet show notable improvements over supervised BigGAN and semisupervised methods. The code is available at https://github.com/raven38/OSSGAN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Katsumata_OSSGAN_Open-Set_Semi-Supervised_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Katsumata_OSSGAN_Open-Set_Semi-Supervised_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.14249 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Katsumata_OSSGAN_Open-Set_Semi-Supervised_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Katsumata_OSSGAN_Open-Set_Semi-Supervised_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Lite Vision Transformer With Enhanced Self-Attention | Chenglin Yang, Yilin Wang, Jianming Zhang, He Zhang, Zijun Wei, Zhe Lin, Alan Yuille | Despite the impressive representation capacity of vision transformer models, current light-weight vision transformer models still suffer from inconsistent and incorrect dense predictions at local regions. We suspect that the power of their self-attention mechanism is limited in shallower and thinner networks. We propose Lite Vision Transformer (LVT), a novel light-weight transformer network with two enhanced self-attention mechanisms to improve the model performances for mobile deployment. For the low-level features, we introduce Convolutional Self-Attention (CSA). Unlike previous approaches of merging convolution and self-attention, CSA introduces local self-attention into the convolution within a kernel of size 3 x 3 to enrich low-level features in the first stage of LVT. For the high-level features, we propose Recursive Atrous Self-Attention (RASA), which utilizes the multi-scale context when calculating the similarity map and a recursive mechanism to increase the representation capability with marginal extra parameter cost. The superiority of LVT is demonstrated on ImageNet recognition, ADE20K semantic segmentation, and COCO panoptic segmentation. The code is made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Lite_Vision_Transformer_With_Enhanced_Self-Attention_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.10809 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Lite_Vision_Transformer_With_Enhanced_Self-Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Lite_Vision_Transformer_With_Enhanced_Self-Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection | Zhuoling Li, Zhan Qu, Yang Zhou, Jianzhuang Liu, Haoqian Wang, Lihui Jiang | As an inherently ill-posed problem, depth estimation from single images is the most challenging part of monocular 3D object detection (M3OD). Many existing methods rely on preconceived assumptions to bridge the missing spatial information in monocular images, and predict a sole depth value for every object of interest. However, these assumptions do not always hold in practical applications. To tackle this problem, we propose a depth solving system that fully explores the visual clues from the subtasks in M3OD and generates multiple estimations for the depth of each target. Since the depth estimations rely on different assumptions in essence, they present diverse distributions. Even if some assumptions collapse, the estimations established on the remaining assumptions are still reliable. In addition, we develop a depth selection and combination strategy. This strategy is able to remove abnormal estimations caused by collapsed assumptions, and adaptively combine the remaining estimations into a single one. In this way, our depth solving system becomes more precise and robust. Exploiting the clues from multiple subtasks of M3OD and without introducing any extra information, our method surpasses the current best method by more than 20% relatively on the Moderate level of test split in the KITTI 3D object detection benchmark, while still maintaining real-time efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Diversity_Matters_Fully_Exploiting_Depth_Clues_for_Reliable_Monocular_3D_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.09373 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Diversity_Matters_Fully_Exploiting_Depth_Clues_for_Reliable_Monocular_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Diversity_Matters_Fully_Exploiting_Depth_Clues_for_Reliable_Monocular_3D_CVPR_2022_paper.html | CVPR 2022 | null |
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning | Tony Ng, Hyo Jin Kim, Vincent T. Lee, Daniel DeTone, Tsun-Yi Yang, Tianwei Shen, Eddy Ilg, Vassileios Balntas, Krystian Mikolajczyk, Chris Sweeney | In the light of recent analyses on privacy-concerning scene revelation from visual descriptors, we develop descriptors that conceal the input image content. In particular, we propose an adversarial learning framework for training visual descriptors that prevent image reconstruction, while maintaining the matching accuracy. We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors. The experimental results demonstrate that the visual descriptors obtained with our method significantly deteriorate the image reconstruction quality with minimal impact on correspondence matching and camera localization performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ng_NinjaDesc_Content-Concealing_Visual_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.12785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ng_NinjaDesc_Content-Concealing_Visual_Descriptors_via_Adversarial_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Physically-Guided Disentangled Implicit Rendering for 3D Face Modeling | Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, Dongjin Huang | This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for high-fidelity 3D face modeling. The motivation comes from two observations: widely-used graphics renderers yield excessive approximations against photo-realistic imaging, while neural rendering methods are highly entangled to perceive 3D-aware operations. Hence, we learn to disentangle the implicit rendering via explicit physical guidance, meanwhile guarantee the properties of (1) 3D-aware comprehension and (2) high-reality imaging. For the former one, PhyDIR explicitly adopts 3D shading and rasterizing modules to control the renderer, which disentangles the lighting, facial shape and view point from neural reasoning. Specifically, PhyDIR proposes a novel multi-image shading strategy to compensate the monocular limitation, so that the lighting variations are accessible to the neural renderer. For the latter one, PhyDIR learns the face-collection implicit texture to avoid ill-posed intrinsic factorization, then leverages a series of consistency losses to constrain the robustness. With the disentangled method, we make 3D face modeling benefit from both kinds of rendering strategies. Extensive experiments on benchmarks show that PhyDIR obtains superior performance than state-of-the-art explicit/implicit methods, on both geometry/texture modeling. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Physically-Guided_Disentangled_Implicit_Rendering_for_3D_Face_Modeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Physically-Guided_Disentangled_Implicit_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Physically-Guided_Disentangled_Implicit_Rendering_for_3D_Face_Modeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Physically-Guided_Disentangled_Implicit_Rendering_for_3D_Face_Modeling_CVPR_2022_paper.html | CVPR 2022 | null |
M5Product: Self-Harmonized Contrastive Learning for E-Commercial Multi-Modal Pretraining | Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C. Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, Xiaodan Liang | Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets. By leveraging the natural suitability of E-commerce, where different modalities capture complementary semantic information, we contribute a large-scale multi-modal pre-training dataset M5Product. The dataset comprises 5 modalities (image, text, table, video, and audio), covers over 6,000 categories and 5,000 attributes, and is 500 times larger than the largest publicly available dataset with a similar number of modalities. Furthermore, M5Product contains incomplete modality pairs and noise while also having a long-tailed distribution, resembling most real-world problems. We further propose Self-harmonized ContrAstive LEarning (SCALE), a novel pretraining framework that integrates the different modalities into a unified model through an adaptive feature fusion mechanism, where the importance of each modality is learned directly from the modality embeddings and impacts the inter-modality contrastive learning and masked tasks within a multi-modal transformer model. We evaluate the current multi-modal pre-training state-of-the-art approaches and benchmark their ability to learn from unlabeled data when faced with the large number of modalities in the M5Product dataset. We conduct extensive experiments on four downstream tasks and demonstrate the superiority of our SCALE model, providing insights into the importance of dataset scale and diversity. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_M5Product_Self-Harmonized_Contrastive_Learning_for_E-Commercial_Multi-Modal_Pretraining_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_M5Product_Self-Harmonized_Contrastive_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_M5Product_Self-Harmonized_Contrastive_Learning_for_E-Commercial_Multi-Modal_Pretraining_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_M5Product_Self-Harmonized_Contrastive_Learning_for_E-Commercial_Multi-Modal_Pretraining_CVPR_2022_paper.html | CVPR 2022 | null |
Bi-Level Alignment for Cross-Domain Crowd Counting | Shenjian Gong, Shanshan Zhang, Jian Yang, Dengxin Dai, Bernt Schiele | Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data. To avoid reliance on such annotations, previous works apply unsupervised domain adaptation (UDA) techniques by transferring knowledge learned from easily accessible synthetic data to real-world datasets. However, current state-of-the-art methods either rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. In this work, we aim to develop a new adversarial learning based method, which is simple and efficient to apply. To reduce the domain gap between the synthetic and real data, we design a bi-level alignment framework (BLA) consisting of (1) task-driven data alignment and (2) fine-grained feature alignment. Contrast to previous domain augmentation methods, we introduce AutoML to search for an optimal transform on source, which well serves for the downstream task. On the other hand, we do fine-grained alignment for foreground and background separately to alleviate the alignment difficulty. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin. Also, our approach is simple, easy to implement and efficient to apply. The code will be made publicly available. https://github.com/Yankeegsj/BLA | https://openaccess.thecvf.com/content/CVPR2022/papers/Gong_Bi-Level_Alignment_for_Cross-Domain_Crowd_Counting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gong_Bi-Level_Alignment_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.05844 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_Bi-Level_Alignment_for_Cross-Domain_Crowd_Counting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_Bi-Level_Alignment_for_Cross-Domain_Crowd_Counting_CVPR_2022_paper.html | CVPR 2022 | null |
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation | Duolikun Danier, Fan Zhang, David Bull | Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding. VFI can be extremely challenging, particularly in sequences containing large motions, occlusions or dynamic textures, where existing approaches fail to offer perceptually robust interpolation performance. In this context, we present a novel deep learning based VFI method, ST-MFNet, based on a Spatio-Temporal Multi-Flow architecture. ST-MFNet employs a new multi-scale multi-flow predictor to estimate many-to-one intermediate flows, which are combined with conventional one-to-one optical flows to capture both large and complex motions. In order to enhance interpolation performance for various textures, a 3D CNN is also employed to model the content dynamics over an extended temporal window. Moreover, ST-MFNet has been trained within an ST-GAN framework, which was originally developed for texture synthesis, with the aim of further improving perceptual interpolation quality. Our approach has been comprehensively evaluated -- compared with fourteen state-of-the-art VFI algorithms -- clearly demonstrating that ST-MFNet consistently outperforms these benchmarks on varied and representative test datasets, with significant gains up to 1.09dB in PSNR for cases including large motions and dynamic textures. Our source code is available at https://github.com/danielism97/ST-MFNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Danier_ST-MFNet_A_Spatio-Temporal_Multi-Flow_Network_for_Frame_Interpolation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Danier_ST-MFNet_A_Spatio-Temporal_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Danier_ST-MFNet_A_Spatio-Temporal_Multi-Flow_Network_for_Frame_Interpolation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Danier_ST-MFNet_A_Spatio-Temporal_Multi-Flow_Network_for_Frame_Interpolation_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites | Ngoc Long Nguyen, Jérémy Anger, Axel Davy, Pablo Arias, Gabriele Facciolo | Modern Earth observation satellites capture multi-exposure bursts of push-frame images that can be super-resolved via computational means. In this work, we propose a super-resolution method for such multi-exposure sequences, a problem that has received very little attention in the literature. The proposed method can handle the signal-dependent noise in the inputs, process sequences of any length, and be robust to inaccuracies in the exposure times. Furthermore, it can be trained end-to-end with self-supervision, without requiring ground truth high resolution frames, which makes it especially suited to handle real data. Central to our method are three key contributions: i) a base-detail decomposition for handling errors in the exposure times, ii) a noise-level-aware feature encoding for improved fusion of frames with varying signal-to-noise ratio and iii) a permutation invariant fusion strategy by temporal pooling operators. We evaluate the proposed method on synthetic and real data and show that it outperforms by a significant margin existing single-exposure approaches that we adapted to the multi-exposure case. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nguyen_Self-Supervised_Super-Resolution_for_Multi-Exposure_Push-Frame_Satellites_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nguyen_Self-Supervised_Super-Resolution_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.02031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nguyen_Self-Supervised_Super-Resolution_for_Multi-Exposure_Push-Frame_Satellites_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nguyen_Self-Supervised_Super-Resolution_for_Multi-Exposure_Push-Frame_Satellites_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Multi-View Stereo by Iterative Dynamic Cost Volume | Shaoqian Wang, Bo Li, Yuchao Dai | In this paper, we propose a novel iterative dynamic cost volume for multi-view stereo. Compared with other works, our cost volume is much lighter, thus could be processed with 2D convolution based GRU. Notably, the every-step output of the GRU could be further used to generate new cost volume. In this way, an iterative GRU-based optimizer is constructed. Furthermore, we present a cascade and hierarchical refinement architecture to utilize the multi-scale information and speed up the convergence. Specifically, a lightweight 3D CNN is utilized to generate the coarsest initial depth map which is essential to launch the GRU and guarantee a fast convergence. Then the depth map is refined by multi-stage GRUs which work on the pyramid feature maps. Extensive experiments on DTU and Tanks & Temples benchmarks demonstrate that our method could achieve state-of-the-art results in terms of accuracy, speed and memory usage. Code will be released at https://github.com/bdwsq1996/Effi-MVS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Efficient_Multi-View_Stereo_by_Iterative_Dynamic_Cost_Volume_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Efficient_Multi-View_Stereo_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Efficient_Multi-View_Stereo_by_Iterative_Dynamic_Cost_Volume_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Efficient_Multi-View_Stereo_by_Iterative_Dynamic_Cost_Volume_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Generate Line Drawings That Convey Geometry and Semantics | Caroline Chan, Frédo Durand, Phillip Isola | This paper presents an unpaired method for creating line drawings from photographs. Current methods often rely on high quality paired datasets to generate line drawings. However, these datasets often have limitations due to the subjects of the drawings belonging to a specific domain, or in the amount of data collected. Although recent work in unsupervised image-to-image translation has shown much progress, the latest methods still struggle to generate compelling line drawings. We observe that line drawings are encodings of scene information and seek to convey 3D shape and semantic meaning. We build these observations into a set of objectives and train an image translation to map photographs into line drawings. We introduce a geometry loss which predicts depth information from the image features of a line drawing, and a semantic loss which matches the CLIP features of a line drawing with its corresponding photograph. Our approach outperforms state-of-the-art unpaired image translation and line drawing generation methods on creating line drawings from arbitrary photographs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chan_Learning_To_Generate_Line_Drawings_That_Convey_Geometry_and_Semantics_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.12691 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Learning_To_Generate_Line_Drawings_That_Convey_Geometry_and_Semantics_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chan_Learning_To_Generate_Line_Drawings_That_Convey_Geometry_and_Semantics_CVPR_2022_paper.html | CVPR 2022 | null |
On Guiding Visual Attention With Language Specification | Suzanne Petryk, Lisa Dunlap, Keyan Nasseri, Joseph Gonzalez, Trevor Darrell, Anna Rohrbach | While real world challenges typically define visual categories with language words or phrases, most visual classification methods define categories with numerical indicies. However, the language specification of the classes provides an especially useful prior for biased and noisy datasets, where it can help disambiguate what features are task-relevant. Recently, large-scale multimodal models have been shown to recognize a wide variety of high-level concepts from a language specification even without additional image training data, but they are often unable to distinguish classes for more fine-grained tasks. CNNs, in contrast, can extract subtle image features that are required for fine-grained discrimination, but will overfit to any bias or noise in datasets. Our insight is to use high-level language specification as advice for constraining the prediction evidence to task-relevant features, instead of distractors. To do this, we ground task-relevant words or phrases with attention maps from a pretrained large-scale model. We then use this grounding to supervise a classifier's spatial attention away from distracting context. We show that supervising spatial attention in this way improves performance on classification tasks with biased and noisy data, including 3-15% worst-group accuracy improvements and 41-45% relative improvements on fairness metrics. | https://openaccess.thecvf.com/content/CVPR2022/papers/Petryk_On_Guiding_Visual_Attention_With_Language_Specification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Petryk_On_Guiding_Visual_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.08926 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Petryk_On_Guiding_Visual_Attention_With_Language_Specification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Petryk_On_Guiding_Visual_Attention_With_Language_Specification_CVPR_2022_paper.html | CVPR 2022 | null |
ReSTR: Convolution-Free Referring Image Segmentation Using Transformers | Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, Suha Kwak | Referring image segmentation is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. Most of existing methods for this task rely heavily on convolutional neural networks, which however have trouble capturing long-range dependencies between entities in the language expression and are not flexible enough for modeling interactions between the two different modalities. To address these issues, we present the first convolution-free model for referring image segmentation using transformers, dubbed ReSTR. Since it extracts features of both modalities through transformer encoders, it can capture long-range dependencies between entities within each modality. Also, ReSTR fuses features of the two modalities by a self-attention encoder, which enables flexible and adaptive interactions between the two modalities in the fusion process. The fused features are fed to a segmentation module, which works adaptively according to the image and language expression in hand. ReSTR is evaluated and compared with previous work on all public benchmarks, where it outperforms all existing models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_ReSTR_Convolution-Free_Referring_Image_Segmentation_Using_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_ReSTR_Convolution-Free_Referring_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16768 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_ReSTR_Convolution-Free_Referring_Image_Segmentation_Using_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_ReSTR_Convolution-Free_Referring_Image_Segmentation_Using_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
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