Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | supp
string | arXiv
string | bibtex
string | url
string | detail_url
string | tags
string | string |
|---|---|---|---|---|---|---|---|---|---|---|
A Graph Matching Perspective With Transformers on Video Instance Segmentation | null | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_A_Graph_Matching_Perspective_With_Transformers_on_Video_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_A_Graph_Matching_Perspective_With_Transformers_on_Video_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing | Yanbo Xu, Yueqin Yin, Liming Jiang, Qianyi Wu, Chengyao Zheng, Chen Change Loy, Bo Dai, Wayne Wu | Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing. Code and models are publicly available at https://github.com/BillyXYB/TransEditor. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_TransEditor_Transformer-Based_Dual-Space_GAN_for_Highly_Controllable_Facial_Editing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_TransEditor_Transformer-Based_Dual-Space_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17266 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_TransEditor_Transformer-Based_Dual-Space_GAN_for_Highly_Controllable_Facial_Editing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_TransEditor_Transformer-Based_Dual-Space_GAN_for_Highly_Controllable_Facial_Editing_CVPR_2022_paper.html | CVPR 2022 | null |
FLAG: Flow-Based 3D Avatar Generation From Sparse Observations | Sadegh Aliakbarian, Pashmina Cameron, Federica Bogo, Andrew Fitzgibbon, Thomas J. Cashman | To represent people in mixed reality applications for collaboration and communication, we need to generate realistic and faithful avatar poses. However, the signal streams that can be applied for this task from head-mounted devices (HMDs) are typically limited to head pose and hand pose estimates. While these signals are valuable, they are an incomplete representation of the human body, making it challenging to generate a faithful full-body avatar. We address this challenge by developing a flow-based generative model of the 3D human body from sparse observations, wherein we learn not only a conditional distribution of 3D human pose, but also a probabilistic mapping from observations to the latent space from which we can generate a plausible pose along with uncertainty estimates for the joints. We show that our approach is not only a strong predictive model, but can also act as an efficient pose prior in different optimization settings where a good initial latent code plays a major role. | https://openaccess.thecvf.com/content/CVPR2022/papers/Aliakbarian_FLAG_Flow-Based_3D_Avatar_Generation_From_Sparse_Observations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Aliakbarian_FLAG_Flow-Based_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05789 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Aliakbarian_FLAG_Flow-Based_3D_Avatar_Generation_From_Sparse_Observations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Aliakbarian_FLAG_Flow-Based_3D_Avatar_Generation_From_Sparse_Observations_CVPR_2022_paper.html | CVPR 2022 | null |
Stability-Driven Contact Reconstruction From Monocular Color Images | Zimeng Zhao, Binghui Zuo, Wei Xie, Yangang Wang | Physical contact provides additional constraints for hand-object state reconstruction as well as a basis for further understanding of interaction affordances. Estimating these severely occluded regions from monocular images presents a considerable challenge. Existing methods optimize the hand-object contact driven by distance threshold or prior from contact-labeled datasets. However, due to the number of subjects and objects involved in these indoor datasets being limited, the learned contact patterns could not generalize easily. Our key idea is to reconstruct the contact pattern directly from monocular images and utilize the physical stability criterion in the simulation to drive the optimization process described above. This criterion is defined by the resultant forces and contact distribution computed by the physics engine. Compared to existing solutions, our framework can be adapted to more personalized hands and diverse object shapes. Furthermore, we create an interaction dataset with extra physical attributes to verify the sim-to-real consistency of our methods. Through comprehensive evaluations, hand-object contact can be reconstructed with both accuracy and stability by the proposed framework. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Stability-Driven_Contact_Reconstruction_From_Monocular_Color_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Stability-Driven_Contact_Reconstruction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.00848 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Stability-Driven_Contact_Reconstruction_From_Monocular_Color_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Stability-Driven_Contact_Reconstruction_From_Monocular_Color_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Use All the Labels: A Hierarchical Multi-Label Contrastive Learning Framework | Shu Zhang, Ran Xu, Caiming Xiong, Chetan Ramaiah | Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint. The loss function is data driven and automatically adapts to arbitrary multi-label structures. Experiments on several datasets show that our relationship-preserving embedding performs well on a variety of tasks and outperform the baseline supervised and self-supervised approaches. Code is available at https://github.com/salesforce/hierarchicalContrastiveLearning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Use_All_the_Labels_A_Hierarchical_Multi-Label_Contrastive_Learning_Framework_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Use_All_the_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13207 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Use_All_the_Labels_A_Hierarchical_Multi-Label_Contrastive_Learning_Framework_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Use_All_the_Labels_A_Hierarchical_Multi-Label_Contrastive_Learning_Framework_CVPR_2022_paper.html | CVPR 2022 | null |
SGTR: End-to-End Scene Graph Generation With Transformer | Rongjie Li, Songyang Zhang, Xuming He | Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up two-stage or a point-based one-stage approach, which often suffers from high time complexity or sub-optimal designs. In this work, we propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To solve the problem, we develop a transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets. In particular, we develop a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on two challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. We hope our model can serve as a strong baseline for the Transformer-based scene graph generation. Code is available in https://github.com/Scarecrow0/SGTR | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_SGTR_End-to-End_Scene_Graph_Generation_With_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_SGTR_End-to-End_Scene_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.12970 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SGTR_End-to-End_Scene_Graph_Generation_With_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SGTR_End-to-End_Scene_Graph_Generation_With_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation | Abhijit Kundu, Kyle Genova, Xiaoqi Yin, Alireza Fathi, Caroline Pantofaru, Leonidas J. Guibas, Andrea Tagliasacchi, Frank Dellaert, Thomas Funkhouser | We present PanopticNeRF, an object-aware neural scene representation that decomposes a scene into a set of objects (things) and background (stuff). Each object is represented by a separate MLP that takes a position, direction, and time and outputs density and radiance. The background is represented by a similar MLP that also outputs semantics. Importantly, the object MLPs are specific to each instance and initialized with meta-learning, and thus can be smaller and faster than previous object-aware approaches, while still leveraging category-specific priors. We propose a system to infer the PanopticNeRF representation from a set of color images. We use off-the-shelf algorithms to predict camera poses, object bounding boxes, object categories, and 2D image semantic segmentations. Then we jointly optimize the MLP weights and bounding box parameters using analysis-by-synthesis with self-supervision from the color images and pseudo-supervision from predicted semantic segmentations. PanopticNeRF can be effectively used for multiple 2D and 3D tasks like 3D scene editing, 3D panoptic reconstruction, novel view and semantic synthesis, 2D panoptic segmentation, and multiview depth prediction. We demonstrate these applications on several difficult, dynamic scenes with moving objects. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kundu_Panoptic_Neural_Fields_A_Semantic_Object-Aware_Neural_Scene_Representation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.04334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kundu_Panoptic_Neural_Fields_A_Semantic_Object-Aware_Neural_Scene_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kundu_Panoptic_Neural_Fields_A_Semantic_Object-Aware_Neural_Scene_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
Texture-Based Error Analysis for Image Super-Resolution | Salma Abdel Magid, Zudi Lin, Donglai Wei, Yulun Zhang, Jinjin Gu, Hanspeter Pfister | Evaluation practices for image super-resolution (SR) use a single-value metric, the PSNR or SSIM, to determine model performance. This provides little insight into the source of errors and model behavior. Therefore, it is beneficial to move beyond the conventional approach and reconceptualize evaluation with interpretability as our main priority. We focus on a thorough error analysis from a variety of perspectives. Our key contribution is to leverage a texture classifier, which enables us to assign patches with semantic labels, to identify the source of SR errors both globally and locally. We then use this to determine (a) the semantic alignment of SR datasets, (b) how SR models perform on each label, (c) to what extent high-resolution (HR) and SR patches semantically correspond, and more. Through these different angles, we are able to highlight potential pitfalls and blindspots. Our overall investigation highlights numerous unexpected insights. We hope this work serves as an initial step for debugging blackbox SR networks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Magid_Texture-Based_Error_Analysis_for_Image_Super-Resolution_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Magid_Texture-Based_Error_Analysis_for_Image_Super-Resolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Magid_Texture-Based_Error_Analysis_for_Image_Super-Resolution_CVPR_2022_paper.html | CVPR 2022 | null |
PILC: Practical Image Lossless Compression With an End-to-End GPU Oriented Neural Framework | Ning Kang, Shanzhao Qiu, Shifeng Zhang, Zhenguo Li, Shu-Tao Xia | Generative model based image lossless compression algorithms have seen a great success in improving compression ratio. However, the throughput for most of them is less than 1 MB/s even with the most advanced AI accelerated chips, preventing them from most real-world applications, which often require 100 MB/s. In this paper, we propose PILC, an end-to-end image lossless compression framework that achieves 200 MB/s for both compression and decompression with a single NVIDIA Tesla V100 GPU, 10x faster than the most efficient one before. To obtain this result, we first develop an AI codec that combines auto-regressive model and VQ-VAE which performs well in lightweight setting, then we design a low complexity entropy coder that works well with our codec. Experiments show that our framework compresses better than PNG by a margin of 30% in multiple datasets. We believe this is an important step to bring AI compression forward to commercial use. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kang_PILC_Practical_Image_Lossless_Compression_With_an_End-to-End_GPU_Oriented_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kang_PILC_Practical_Image_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_PILC_Practical_Image_Lossless_Compression_With_an_End-to-End_GPU_Oriented_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_PILC_Practical_Image_Lossless_Compression_With_an_End-to-End_GPU_Oriented_CVPR_2022_paper.html | CVPR 2022 | null |
Set-Supervised Action Learning in Procedural Task Videos via Pairwise Order Consistency | Zijia Lu, Ehsan Elhamifar | We address the problem of set-supervised action learning, whose goal is to learn an action segmentation model using weak supervision in the form of sets of actions occurring in training videos. Our key observation is that videos within the same task have similar ordering of actions, which can be leveraged for effective learning. Therefore, we propose an attention-based method with a new Pairwise Ordering Consistency (POC) loss that encourages that for each common action pair in two videos of the same task, the attentions of actions follow a similar ordering. Unlike existing sequence alignment methods, which misalign actions in videos with different orderings or cannot reliably separate more from less consistent orderings, our POC loss efficiently aligns videos with different action orders and is differentiable, which enables end-to-end training. In addition, it avoids the time-consuming pseudo-label generation of prior works. Our method efficiently learns the actions and their temporal locations, therefore, extends the existing attention-based action localization methods from learning one action per video to multiple actions using our POC loss along with video-level and frame-level losses. By experiments on three datasets, we demonstrate that our method significantly improves the state of the art. We also show that our method, with a small modification, can effectively address the transcript-supervised action learning task, where actions and their ordering are available during training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Set-Supervised_Action_Learning_in_Procedural_Task_Videos_via_Pairwise_Order_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Set-Supervised_Action_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Set-Supervised_Action_Learning_in_Procedural_Task_Videos_via_Pairwise_Order_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Set-Supervised_Action_Learning_in_Procedural_Task_Videos_via_Pairwise_Order_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Align Sequential Actions in the Wild | Weizhe Liu, Bugra Tekin, Huseyin Coskun, Vibhav Vineet, Pascal Fua, Marc Pollefeys | State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame mapping across sequences, which does not leverage temporal information, or assume monotonic alignment between each video pair, which ignores variations in the order of actions. As such, these methods are not able to deal with common real-world scenarios that involve background frames or videos that contain non-monotonic sequence of actions. In this paper, we propose an approach to align sequential actions in the wild that involve diverse temporal variations. To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions. Our model accounts for both monotonic and non-monotonic sequences and handles background frames that should not be aligned. We demonstrate that our approach consistently outperforms the state-of-the-art in self-supervised sequential action representation learning on four different benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_To_Align_Sequential_Actions_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_To_Align_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09301 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Align_Sequential_Actions_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_To_Align_Sequential_Actions_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
Decoupled Knowledge Distillation | Borui Zhao, Quan Cui, Renjie Song, Yiyu Qiu, Jiajun Liang | State-of-the-art distillation methods are mainly based on distilling deep features from intermediate layers, while the significance of logit distillation is greatly overlooked. To provide a novel viewpoint to study logit distillation, we reformulate the classical KD loss into two parts, i.e., target class knowledge distillation (TCKD) and non-target class knowledge distillation (NCKD). We empirically investigate and prove the effects of the two parts: TCKD transfers knowledge concerning the "difficulty" of training samples, while NCKD is the prominent reason why logit distillation works. More importantly, we reveal that the classical KD loss is a coupled formulation, which (1) suppresses the effectiveness of NCKD and (2) limits the flexibility to balance these two parts. To address these issues, we present Decoupled Knowledge Distillation(DKD), enabling TCKD and NCKD to play their roles more efficiently and flexibly. Compared with complex feature-based methods, our DKD achieves comparable or even better results and has better training efficiency on CIFAR-100, ImageNet, and MS-COCO datasets for image classification and object detection tasks. This paper proves the great potential of logit distillation, and we hope it will be helpful for future research. The code is available at https://github.com/megvii-research/mdistiller. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Decoupled_Knowledge_Distillation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Decoupled_Knowledge_Distillation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Decoupled_Knowledge_Distillation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Decoupled_Knowledge_Distillation_CVPR_2022_paper.html | CVPR 2022 | null |
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection | Yingwei Li, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan Yuille, Mingxing Tan | Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods simply decorate raw lidar point clouds with camera features and feed them directly to existing 3D detection models, our study shows that fusing camera features with deep lidar features instead of raw points, can lead to better performance. However, as those features are often augmented and aggregated, a key challenge in fusion is how to effectively align the transformed features from two modalities. In this paper, we propose two novel techniques: InverseAug that inverses geometric-related augmentations, e.g., rotation, to enable accurate geometric alignment between lidar points and image pixels, and LearnableAlign that leverages cross-attention to dynamically capture the correlations between image and lidar features during fusion. Based on InverseAug and LearnableAlign, we develop a family of generic multi-modal 3D detection models named DeepFusion, which is more accurate than previous methods. For example, DeepFusion improves PointPillars, CenterPoint, and 3D-MAN baselines on Pedestrian detection for 6.7, 8.9, and 6.2 LEVEL_2 APH, respectively. Notably, our models achieve state-of-the-art performance on Waymo Open Dataset, and show strong model robustness against input corruptions and out-of-distribution data. Code will be publicly available at https://github.com/tensorflow/lingvo. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_DeepFusion_Lidar-Camera_Deep_Fusion_for_Multi-Modal_3D_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_DeepFusion_Lidar-Camera_Deep_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DeepFusion_Lidar-Camera_Deep_Fusion_for_Multi-Modal_3D_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_DeepFusion_Lidar-Camera_Deep_Fusion_for_Multi-Modal_3D_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Volumetric Object Selection | Zhongzheng Ren, Aseem Agarwala, Bryan Russell, Alexander G. Schwing, Oliver Wang | We introduce an approach for selecting objects in neural volumetric 3D representations, such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a set of foreground and background 2D user scribbles in one view and automatically estimates a 3D segmentation of the desired object, which can be rendered into novel views. To achieve this result, we propose a novel voxel feature embedding that incorporates the neural volumetric 3D representation and multi-view image features from all input views. To evaluate our approach, we introduce a new dataset of human-provided segmentation masks for depicted objects in real-world multi-view scene captures. We show that our approach out-performs strong baselines, including 2D segmentation and 3D segmentation approaches adapted to our task. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Neural_Volumetric_Object_Selection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.14929 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Neural_Volumetric_Object_Selection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Neural_Volumetric_Object_Selection_CVPR_2022_paper.html | CVPR 2022 | null |
GCR: Gradient Coreset Based Replay Buffer Selection for Continual Learning | Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, Pradeep Shenoy | Continual learning (CL) aims to develop techniques by which a single model adapts to an increasing number of tasks encountered sequentially, thereby potentially leveraging learnings across tasks in a resource-efficient manner. A major challenge for CL systems is catastrophic forgetting, where earlier tasks are forgotten while learning a new task. To address this, replay-based CL approaches maintain and repeatedly retrain on a small buffer of data selected across encountered tasks. We propose Gradient Coreset Replay (GCR), a novel strategy for replay buffer selection and update using a carefully designed optimization criterion. Specifically, we select and maintain a 'coreset' that closely approximates the gradient of all the data seen so far with respect to current model parameters, and discuss key strategies needed for its effective application to the continual learning setting. We show significant gains (2%-4% absolute) over the state-of-the-art in the well-studied offline continual learning setting. Our findings also effectively transfer to online / streaming CL settings, showing up to 5% gains over existing approaches. Finally, we demonstrate the value of supervised contrastive loss for continual learning, which yields a cumulative gain of up to 5% accuracy when combined with our subset selection strategy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tiwari_GCR_Gradient_Coreset_Based_Replay_Buffer_Selection_for_Continual_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tiwari_GCR_Gradient_Coreset_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.11210 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tiwari_GCR_Gradient_Coreset_Based_Replay_Buffer_Selection_for_Continual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tiwari_GCR_Gradient_Coreset_Based_Replay_Buffer_Selection_for_Continual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
PointCLIP: Point Cloud Understanding by CLIP | Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, Hongsheng Li | Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to match images with their corresponding texts in open-vocabulary settings. However, it remains under explored that whether CLIP, pre-trained by large-scale image-text pairs in 2D, can be generalized to 3D recognition. In this paper, we identify such a setting is feasible by proposing PointCLIP, which conducts alignment between CLIP-encoded point cloud and 3D category texts. Specifically, we encode a point cloud by projecting it into multi-view depth maps without rendering, and aggregate the view-wise zero-shot prediction to achieve knowledge transfer from 2D to 3D. On top of that, we design an inter-view adapter to better extract the global feature and adaptively fuse the few-shot knowledge learned from 3D into CLIP pre-trained in 2D. By just fine-tuning the lightweight adapter in the few-shot settings, the performance of PointCLIP could be largely improved. In addition, we observe the complementary property between PointCLIP and classical 3D-supervised networks. By simple ensembling, PointCLIP boosts baseline's performance and even surpasses state-of-the-art models. Therefore, PointCLIP is a promising alternative for effective 3D point cloud understanding via CLIP under low resource cost and data regime. We conduct thorough experiments on widely-adopted ModelNet10, ModelNet40 and the challenging ScanObjectNN to demonstrate the effectiveness of PointCLIP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_PointCLIP_Point_Cloud_Understanding_by_CLIP_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_PointCLIP_Point_Cloud_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02413 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_PointCLIP_Point_Cloud_Understanding_by_CLIP_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_PointCLIP_Point_Cloud_Understanding_by_CLIP_CVPR_2022_paper.html | CVPR 2022 | null |
NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction | Xiaoshuai Zhang, Sai Bi, Kalyan Sunkavalli, Hao Su, Zexiang Xu | While NeRF has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale indoor scenes. In contrast, classical 3D reconstruction methods can handle large-scale scenes but do not produce realistic renderings. We propose NeRFusion, a method that combines the advantages of NeRF and TSDF-based fusion techniques to achieve efficient large-scale reconstruction and photo-realistic rendering. We process the input image sequence to predict per-frame local radiance fields via direct network inference. These are then fused using a novel recurrent neural network that incrementally reconstructs a global, sparse scene representation in real-time. This global volume can be further fine-tuned to boost rendering quality. We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_NeRFusion_Fusing_Radiance_Fields_for_Large-Scale_Scene_Reconstruction_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.11283 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_NeRFusion_Fusing_Radiance_Fields_for_Large-Scale_Scene_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_NeRFusion_Fusing_Radiance_Fields_for_Large-Scale_Scene_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
DeepFace-EMD: Re-Ranking Using Patch-Wise Earth Mover's Distance Improves Out-of-Distribution Face Identification | Hai Phan, Anh Nguyen | Face identification (FI) is ubiquitous and drives many high-stake decisions made by the law enforcement. State-of-the-art FI approaches compare two images by taking the cosine similarity between their image embeddings. Yet, such approach suffers from poor out-of-distribution (OOD) generalization to new types of images (e.g., when a query face is masked, cropped or rotated) not included in the training set or the gallery. Here, we propose a re-ranking approach that compares two faces using the Earth Mover's Distance on the deep, spatial features of image patches. Our extra comparison stage explicitly examines image similarity at a fine-grained level (e.g., eyes to eyes) and is more robust to OOD perturbations and occlusions than traditional FI. Interestingly, without finetuning feature extractors, our method consistently improves the accuracy on all tested OOD queries: masked, cropped, rotated, and adversarial while obtaining similar results on in-distribution images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Phan_DeepFace-EMD_Re-Ranking_Using_Patch-Wise_Earth_Movers_Distance_Improves_Out-of-Distribution_Face_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Phan_DeepFace-EMD_Re-Ranking_Using_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Phan_DeepFace-EMD_Re-Ranking_Using_Patch-Wise_Earth_Movers_Distance_Improves_Out-of-Distribution_Face_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Phan_DeepFace-EMD_Re-Ranking_Using_Patch-Wise_Earth_Movers_Distance_Improves_Out-of-Distribution_Face_CVPR_2022_paper.html | CVPR 2022 | null |
A Sampling-Based Approach for Efficient Clustering in Large Datasets | Georgios Exarchakis, Omar Oubari, Gregor Lenz | We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our contribution is substantially more efficient than k-means as it does not require an all to all comparison of data points and clusters. We show that the optimal solutions of our approximation are the same as in the exact solution. However, our approach is considerably more efficient at extracting these clusters compared to the state-of-the-art. We compare our approximation with the exact k-means and alternative approximation approaches on a series of standardised clustering tasks. For the evaluation, we consider the algorithmic complexity, including the number of operations until convergence, and the stability of the results. An efficient implementation of the algorithm is provided in online. | https://openaccess.thecvf.com/content/CVPR2022/papers/Exarchakis_A_Sampling-Based_Approach_for_Efficient_Clustering_in_Large_Datasets_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.14793 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Exarchakis_A_Sampling-Based_Approach_for_Efficient_Clustering_in_Large_Datasets_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Exarchakis_A_Sampling-Based_Approach_for_Efficient_Clustering_in_Large_Datasets_CVPR_2022_paper.html | CVPR 2022 | null |
General Facial Representation Learning in a Visual-Linguistic Manner | Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, Fang Wen | How to learn a universal facial representation that boosts all face analysis tasks This paper takes one step toward this goal. In this paper, we study the transfer performance of pre-trained models on face analysis tasks and introduce a framework, called FaRL, for general facial representation learning. On one hand, the framework involves a contrastive loss to learn high-level semantic meaning from image-text pairs. On the other hand, we propose exploring low-level information simultaneously to further enhance the face representation by adding a masked image modeling. We perform pre-training on LAION-FACE, a dataset containing a large amount of face image-text pairs, and evaluate the representation capability on multiple downstream tasks. We show that FaRL achieves better transfer performance compared with previous pre-trained models. We also verify its superiority in the low-data regime. More importantly, our model surpasses the state-of-the-art methods on face analysis tasks including face parsing and face alignment. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_General_Facial_Representation_Learning_in_a_Visual-Linguistic_Manner_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_General_Facial_Representation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03109 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_General_Facial_Representation_Learning_in_a_Visual-Linguistic_Manner_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_General_Facial_Representation_Learning_in_a_Visual-Linguistic_Manner_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Color Consistent Network for Low-Light Image Enhancement | Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, Meng Wang | Low-light image enhancement focus on refining the illumination and keep naturalness to obtain the normal-light image. Current low-light image enhancement methods can well improve the illumination. However, there is still color difference between the enhanced image and the ground-truth image. To alleviate this issue, we therefore propose deep color consistent network (DCC-Net) to preserve the color consistency for low-light enhancement. In this paper, we decouple a color image to two main components, a gray image and a color hist histogram. Further, we employ these two components to guide the enhancement, where the gray image is used to generate reasonable structures and textures and the corresponding color histogram is beneficial to keeping color consistency. To reduce the gap between images and color histograms, we also develop a pyramid color embedding (PCE) module, which can better embed the color information to the enhancement process according to the affinity between images and color histograms. Extensive experiments on the LOL, DICM, LIME, MEF, NPE and VV demonstrate that DCC-Net can well preserve color consistency, and performs favorably against state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Deep_Color_Consistent_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Deep_Color_Consistent_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Deep_Color_Consistent_Network_for_Low-Light_Image_Enhancement_CVPR_2022_paper.html | CVPR 2022 | null |
AdaSTE: An Adaptive Straight-Through Estimator To Train Binary Neural Networks | Huu Le, Rasmus Kjær Høier, Che-Tsung Lin, Christopher Zach | We propose a new algorithm for training deep neural networks (DNNs) with binary weights. In particular, we first cast the problem of training binary neural networks (BiNNs) as a bilevel optimization instance and subsequently construct flexible relaxations of this bilevel program. The resulting training method shares its algorithmic simplicity with several existing approaches to train BiNNs, in particular with the straight-through gradient estimator successfully employed in BinaryConnect and subsequent methods. In fact, our proposed method can be interpreted as an adaptive variant of the original straight-through estimator that conditionally (but not always) acts like a linear mapping in the backward pass of error propagation. Experimental results demonstrate that our new algorithm offers favorable performance compared to existing approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Le_AdaSTE_An_Adaptive_Straight-Through_Estimator_To_Train_Binary_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Le_AdaSTE_An_Adaptive_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Le_AdaSTE_An_Adaptive_Straight-Through_Estimator_To_Train_Binary_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Le_AdaSTE_An_Adaptive_Straight-Through_Estimator_To_Train_Binary_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Reusing the Task-Specific Classifier as a Discriminator: Discriminator-Free Adversarial Domain Adaptation | Lin Chen, Huaian Chen, Zhixiang Wei, Xin Jin, Xiao Tan, Yi Jin, Enhong Chen | Adversarial learning has achieved remarkable performances for unsupervised domain adaptation (UDA). Existing adversarial UDA methods typically adopt an additional discriminator to play the min-max game with a feature extractor. However, most of these methods failed to effectively leverage the predicted discriminative information, and thus cause mode collapse for generator. In this work, we address this problem from a different perspective and design a simple yet effective adversarial paradigm in the form of a discriminator-free adversarial learning network (DALN), wherein the category classifier is reused as a discriminator, which achieves explicit domain alignment and category distinguishment through a unified objective, enabling the DALN to leverage the predicted discriminative information for sufficient feature alignment. Basically, we introduce a Nuclear-norm Wasserstein discrepancy (NWD) that has definite guidance meaning for performing discrimination. Such NWD can be coupled with the classifier to serve as a discriminator satisfying the K-Lipschitz constraint without the requirements of additional weight clipping or gradient penalty strategy. Without bells and whistles, DALN compares favorably against the existing state-of-the-art (SOTA) methods on a variety of public datasets. Moreover, as a plug-and-play technique, NWD can be directly used as a generic regularizer to benefit existing UDA algorithms. Code is available at https://github.com/xiaoachen98/DALN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Reusing_the_Task-Specific_Classifier_as_a_Discriminator_Discriminator-Free_Adversarial_Domain_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Reusing_the_Task-Specific_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Reusing_the_Task-Specific_Classifier_as_a_Discriminator_Discriminator-Free_Adversarial_Domain_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Reusing_the_Task-Specific_Classifier_as_a_Discriminator_Discriminator-Free_Adversarial_Domain_CVPR_2022_paper.html | CVPR 2022 | null |
Pooling Revisited: Your Receptive Field Is Suboptimal | Dong-Hwan Jang, Sanghyeok Chu, Joonhyuk Kim, Bohyung Han | The size and shape of the receptive field determine how the network aggregates local features, and affect the overall performance of a model considerably. Many components in a neural network, such as depth, kernel sizes, and strides for convolution and pooling, influence the receptive field. However, they still rely on hyperparameters, and the receptive fields of existing models result in suboptimal shapes and sizes. Hence, we propose a simple yet effective Dynamically Optimized Pooling operation, referred to as DynOPool, which learns the optimized scale factors of feature maps end-to-end. Moreover, DynOPool determines the proper resolution of a feature map by learning the desirable size and shape of its receptive field, which allows an operator in a deeper layer to observe an input image in the optimal scale. Any kind of resizing modules in a deep neural network can be replaced by DynOPool with minimal cost. Also, DynOPool controls the complexity of the model by introducing an additional loss term that constrains computational cost. Our experiments show that the models equipped with the proposed learnable resizing module outperform the baseline algorithms on multiple datasets in image classification and semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jang_Pooling_Revisited_Your_Receptive_Field_Is_Suboptimal_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jang_Pooling_Revisited_Your_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.15254 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jang_Pooling_Revisited_Your_Receptive_Field_Is_Suboptimal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jang_Pooling_Revisited_Your_Receptive_Field_Is_Suboptimal_CVPR_2022_paper.html | CVPR 2022 | null |
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches | Jin-Man Park, Ue-Hwan Kim, Seon-Hoon Lee, Jong-Hwan Kim | Accurate change detection enables a wide range of tasks in visual surveillance, anomaly detection and mobile robotics. However, contemporary change detection approaches assume an ideal matching between the current and stored scenes, whereas only coarse matching is possible in real-world scenarios. Thus, contemporary approaches fail to show the reported performance in real-world settings. To overcome this limitation, we propose SimSaC. SimSaC concurrently conducts scene flow estimation and change detection and is able to detect changes with imperfect matches. To train SimSaC without additional manual labeling, we propose a training scheme with random geometric transformations and the cut-paste method. Moreover, we design an evaluation protocol which reflects performance in real-world settings. In designing the protocol, we collect a test benchmark dataset, which we claim as another contribution. Our comprehensive experiments verify that SimSaC displays robust performance even given imperfect matches and the performance margin compared to contemporary approaches is huge. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Dual_Task_Learning_by_Leveraging_Both_Dense_Correspondence_and_Mis-Correspondence_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Dual_Task_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Dual_Task_Learning_by_Leveraging_Both_Dense_Correspondence_and_Mis-Correspondence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Dual_Task_Learning_by_Leveraging_Both_Dense_Correspondence_and_Mis-Correspondence_CVPR_2022_paper.html | CVPR 2022 | null |
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning | Ligong Han, Jian Ren, Hsin-Ying Lee, Francesco Barbieri, Kyle Olszewski, Shervin Minaee, Dimitris Metaxas, Sergey Tulyakov | Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an image to generate a specific motion trajectory desired by the user since there is no means to provide motion information. Conversely, language information can describe the desired motion, while not precisely defining the content of the video. This work presents a multimodal video generation framework that benefits from text and images provided jointly or separately. We leverage the recent progress in quantized representations for videos and apply a bidirectional transformer with multiple modalities as inputs to predict a discrete video representation. To improve video quality and consistency, we propose a new video token trained with self-learning and an improved mask-prediction algorithm for sampling video tokens. We introduce text augmentation to improve the robustness of the textual representation and diversity of generated videos. Our framework can incorporate various visual modalities, such as segmentation masks, drawings, and partially occluded images. It can generate much longer sequences than the one used for training. In addition, our model can extract visual information as suggested by the text prompt, e.g., "an object in image one is moving northeast", and generate corresponding videos. We run evaluations on three public datasets and a newly collected dataset labeled with facial attributes, achieving state-of-the-art generation results on all four. [Code](https://github.com/snap-research/MMVID) and [webpage](https://snap-research.github.io/MMVID/). | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Show_Me_What_and_Tell_Me_How_Video_Synthesis_via_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Show_Me_What_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02573 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Show_Me_What_and_Tell_Me_How_Video_Synthesis_via_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Show_Me_What_and_Tell_Me_How_Video_Synthesis_via_CVPR_2022_paper.html | CVPR 2022 | null |
Patch Slimming for Efficient Vision Transformers | Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, Dacheng Tao | This paper studies the efficiency problem for visual transformers by excavating redundant calculation in given networks. The recent transformer architecture has demonstrated its effectiveness for achieving excellent performance on a series of computer vision tasks. However, similar to that of convolutional neural networks, the huge computational cost of vision transformers is still a severe issue. Considering that the attention mechanism aggregates different patches layer-by-layer, we present a novel patch slimming approach that discards useless patches in a top-down paradigm. We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers. For each layer, the impact of a patch on the final output feature is approximated and patches with less impacts will be removed. Experimental results on benchmark datasets demonstrate that the proposed method can significantly reduce the computational costs of vision transformers without affecting their performances. For example, over 45% FLOPs of the ViT-Ti model can be reduced with only 0.2% top-1 accuracy drop on the ImageNet dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Patch_Slimming_for_Efficient_Vision_Transformers_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2106.02852 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Patch_Slimming_for_Efficient_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Patch_Slimming_for_Efficient_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Bijective Mapping Network for Shadow Removal | Yurui Zhu, Jie Huang, Xueyang Fu, Feng Zhao, Qibin Sun, Zheng-Jun Zha | Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxiliary supervision of shadow generation on shadow removal procedure. In this work, we argue that shadow removal and generation are interrelated and could provide useful informative supervision for each other. Specifically, we propose a new Bijective Mapping Network (BMNet), which couples the learning procedures of shadow removal and shadow generation in a unified parameter-shared framework. With consistent two-way constraints and synchronous optimization of the two procedures, BMNet could effectively recover the underlying background contents during the forward shadow removal procedure. In addition, through statistic analysis on real-world datasets, we observe and verify that shadow appearances under different color spectrums are inconsistent. This motivates us to design a Shadow-Invariant Color Guidance Module (SICGM), which can explicitly utilize the learned shadow-invariant color information to guide network color restoration, thereby further reducing color-bias effects. Experiments on the representative ISTD, ISTD+ and SRD benchmarks show that our proposed network outperforms the state-of-the-art method [??] in de-shadowing performance, while only using its 0.25% network parameters and 6.25% floating point operations (FLOPs). | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Bijective_Mapping_Network_for_Shadow_Removal_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Bijective_Mapping_Network_for_Shadow_Removal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Bijective_Mapping_Network_for_Shadow_Removal_CVPR_2022_paper.html | CVPR 2022 | null |
End-to-End Semi-Supervised Learning for Video Action Detection | Akash Kumar, Yogesh Singh Rawat | In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-temporal localization of actions. Therefore, we investigate two types of constraints, classification consistency, and spatio-temporal consistency. The presence of predominant background and static regions in a video makes it challenging to utilize spatio-temporal consistency for action detection. To address this, we propose two novel regularization constraints for spatio-temporal consistency; 1) temporal coherency, and 2) gradient smoothness. Both these aspects exploit the temporal continuity of action in videos and are found to be effective for utilizing unlabeled videos for action detection. We demonstrate the effectiveness of the proposed approach on two different action detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show the effectiveness of the proposed approach for video object segmentation on the Youtube-VOS which demonstrates its generalization capability The proposed approach achieves competitive performance by using merely 20% of annotations on UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively, compared to supervised approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kumar_End-to-End_Semi-Supervised_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kumar_End-to-End_Semi-Supervised_Learning_for_Video_Action_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Causal Transportability for Visual Recognition | Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick | Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poorly on out-of-distribution samples because spurious correlations between non-robust features and labels can be changed in a new environment. By analyzing procedures for out-of-distribution generalization with a causal graph, we show that standard classifiers fail because the association between images and labels is not transportable across settings. However, we then show that the causal effect, which severs all sources of confounding, remains invariant across domains. This motivates us to develop an algorithm to estimate the causal effect for image classification, which is transportable (i.e., invariant) across source and target environments. Without observing additional variables, we show that we can derive an estimand for the causal effect under empirical assumptions using representations in deep models as proxies. Theoretical analysis, empirical results, and visualizations show that our approach captures causal invariances and improves overall generalization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mao_Causal_Transportability_for_Visual_Recognition_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.12363 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mao_Causal_Transportability_for_Visual_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mao_Causal_Transportability_for_Visual_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Local Attention Pyramid for Scene Image Generation | Sang-Heon Shim, Sangeek Hyun, DaeHyun Bae, Jae-Pil Heo | In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shim_Local_Attention_Pyramid_for_Scene_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shim_Local_Attention_Pyramid_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shim_Local_Attention_Pyramid_for_Scene_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shim_Local_Attention_Pyramid_for_Scene_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation | Hengbo Ma, Jiachen Li, Ramtin Hosseini, Masayoshi Tomizuka, Chiho Choi | Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving. Recent research has explored several techniques to enhance diversity and maintain the accuracy of human motion prediction at the same time. However, most of them need to define a combined loss, such as the weighted sum of accuracy loss and diversity loss, and then decide their weights as hyperparameters before training. In this work, we aim to design a prediction framework that can balance the accuracy sampling and diversity sampling during the testing phase. In order to achieve this target, we propose a multi-objective conditional variational inference prediction model. We also propose a short-term oracle to encourage the prediction framework to explore more diverse future motions. We evaluate the performance of our proposed approach on two standard human motion datasets. The experiment results show that our approach is effective and on a par with state-of-the-art performance in terms of accuracy and diversity. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Multi-Objective_Diverse_Human_Motion_Prediction_With_Knowledge_Distillation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Multi-Objective_Diverse_Human_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Multi-Objective_Diverse_Human_Motion_Prediction_With_Knowledge_Distillation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Multi-Objective_Diverse_Human_Motion_Prediction_With_Knowledge_Distillation_CVPR_2022_paper.html | CVPR 2022 | null |
GridShift: A Faster Mode-Seeking Algorithm for Image Segmentation and Object Tracking | Abhishek Kumar, Oladayo S. Ajani, Swagatam Das, Rammohan Mallipeddi | In machine learning, MeanShift is one of the popular clustering algorithms. It iteratively moves each data point to the weighted mean of its neighborhood data points. The computational cost required for finding neighborhood data points for each one is quadratic to the number of data points. Therefore, it is very slow for large-scale datasets. To address this issue, we propose a mode-seeking algorithm, GridShift, with faster computing and principally based on MeanShift that uses a grid-based approach. To speed up, GridShift employs a grid-based approach for neighbor search, which is linear to the number of data points. In addition, GridShift moves the active grid cells (grid cells associated with at least one data point) in place of data points towards the higher density, which provides more speed up. The runtime of GridShift is linear to the number of active grid cells and exponential to the number of features. Therefore, it is ideal for large-scale low-dimensional applications such as object tracking and image segmentation. Through extensive experiments, we showcase the superior performance of GridShift compared to other MeanShift-based algorithms and state-of-the-art algorithms in terms of accuracy and runtime on benchmark datasets, image segmentation. Finally, we provide a new object-tracking algorithm based on GridShift and show promising results for object tracking compared to camshift and MeanShift++. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kumar_GridShift_A_Faster_Mode-Seeking_Algorithm_for_Image_Segmentation_and_Object_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kumar_GridShift_A_Faster_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kumar_GridShift_A_Faster_Mode-Seeking_Algorithm_for_Image_Segmentation_and_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kumar_GridShift_A_Faster_Mode-Seeking_Algorithm_for_Image_Segmentation_and_Object_CVPR_2022_paper.html | CVPR 2022 | null |
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors | Yichun Shen, Wanli Jiang, Zhen Xu, Rundong Li, Junghyun Kwon, Siyi Li | It's been a long history that most object detection methods obtain objects by using the non-maximum suppression (NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes. We challenge those NMS-based methods from three aspects: 1) The bounding box with highest confidence value may not be the true positive having the biggest overlap with the ground-truth box. 2) Not only suppression is required for redundant boxes, but also confidence enhancement is needed for those true positives. 3) Sorting candidate boxes by confidence values is not necessary so that full parallelism is achievable. In this paper, inspired by belief propagation (BP), we propose the Confidence Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully parallelizable as well as better in accuracy. In CP-Cluster, we borrow the message passing mechanism from BP to penalize redundant boxes and enhance true positives simultaneously in an iterative way until convergence. We verified the effectiveness of CP-Cluster by applying it to various mainstream detectors such as FasterRCNN, SSD, FCOS, YOLOv3, YOLOv5, Centernet etc. Experiments on MS COCO show that our plug and play method, without retraining detectors, is able to steadily improve average mAP of all those state-of-theart models with a clear margin from 0.3 to 1.9 respectively when compared with NMS-based methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shen_Confidence_Propagation_Cluster_Unleash_Full_Potential_of_Object_Detectors_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.00342 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_Confidence_Propagation_Cluster_Unleash_Full_Potential_of_Object_Detectors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_Confidence_Propagation_Cluster_Unleash_Full_Potential_of_Object_Detectors_CVPR_2022_paper.html | CVPR 2022 | null |
Cluster-Guided Image Synthesis With Unconditional Models | Markos Georgopoulos, James Oldfield, Grigorios G. Chrysos, Yannis Panagakis | Generative Adversarial Networks (GANs) are the driving force behind the state-of-the-art in image generation. Despite their ability to synthesize high-resolution photo-realistic images, generating content with on-demand conditioning of different granularity remains a challenge. This challenge is usually tackled by annotating massive datasets with the attributes of interest, a laborious task that is not always a viable option. Therefore, it is vital to introduce control into the generation process of unsupervised generative models. In this work, we focus on controllable image generation by leveraging GANs that are well-trained in an unsupervised fashion. To this end, we discover that the representation space of intermediate layers of the generator forms a number of clusters that separate the data according to semantically meaningful attributes (e.g., hair color and pose). By conditioning on the cluster assignments, the proposed method is able to control the semantic class of the generated image. Our approach enables sampling from each cluster by Implicit Maximum Likelihood Estimation (IMLE). We showcase the efficacy of our approach on faces (CelebA-HQ and FFHQ), animals (Imagenet) and objects (LSUN) using different pre-trained generative models. The results highlight the ability of our approach to condition image generation on attributes like gender, pose and hair style on faces, as well as a variety of features on different object classes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Georgopoulos_Cluster-Guided_Image_Synthesis_With_Unconditional_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Georgopoulos_Cluster-Guided_Image_Synthesis_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.12911 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Georgopoulos_Cluster-Guided_Image_Synthesis_With_Unconditional_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Georgopoulos_Cluster-Guided_Image_Synthesis_With_Unconditional_Models_CVPR_2022_paper.html | CVPR 2022 | null |
ISNet: Shape Matters for Infrared Small Target Detection | Mingjin Zhang, Rui Zhang, Yuxiang Yang, Haichen Bai, Jing Zhang, Jie Guo | Infrared small target detection (IRSTD) refers to extracting small and dim targets from blurred backgrounds, which has a wide range of applications such as traffic management and marine rescue. Due to the low signal-to-noise ratio and low contrast, infrared targets are easily submerged in the background of heavy noise and clutter. How to detect the precise shape information of infrared targets remains challenging. In this paper, we propose a novel infrared shape network (ISNet), where Taylor finite difference (TFD)-inspired edge block and two-orientation attention aggregation (TOAA) block are devised to address this problem. Specifically, TFD-inspired edge block aggregates and enhances the comprehensive edge information from different levels, in order to improve the contrast between target and background and also lay a foundation for extracting shape information with mathematical interpretation. TOAA block calculates the low-level information with attention mechanism in both row and column directions and fuses it with the high-level information to capture the shape characteristic of targets and suppress noises. In addition, we construct a new benchmark consisting of 1,000 realistic images in various target shapes, different target sizes, and rich clutter backgrounds with accurate pixel-level annotations, called IRSTD-1k. Experiments on public datasets and IRSTD-1k demonstrate the superiority of our approach over representative state-of-the-art IRSTD methods. The dataset and code are available at github.com/RuiZhang97/ISNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_ISNet_Shape_Matters_for_Infrared_Small_Target_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_ISNet_Shape_Matters_for_Infrared_Small_Target_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_ISNet_Shape_Matters_for_Infrared_Small_Target_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Region Feature Synthesizer for Zero-Shot Object Detection | Peiliang Huang, Junwei Han, De Cheng, Dingwen Zhang | Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. The former is used to realize the one-to-more mapping to obtain diverse visual features from each class semantic vector, preventing miss-classifying the real unseen objects as image backgrounds. While the latter is used to avoid the synthesized features too scattered to mix up the inter-class and foreground-background relationship. To demonstrate the effectiveness of the proposed approach, comprehensive experiments on PASCAL VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the new state-of-the-art performance on PASCAL VOC and COCO and it is the first study to carry out zero-shot object detection in remote sensing imagery. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Robust_Region_Feature_Synthesizer_for_Zero-Shot_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Robust_Region_Feature_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.00103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Robust_Region_Feature_Synthesizer_for_Zero-Shot_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Robust_Region_Feature_Synthesizer_for_Zero-Shot_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry | Wei-Chiu Ma, Anqi Joyce Yang, Shenlong Wang, Raquel Urtasun, Antonio Torralba | Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Virtual_Correspondence_Humans_as_a_Cue_for_Extreme-View_Geometry_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Virtual_Correspondence_Humans_as_a_Cue_for_Extreme-View_Geometry_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Virtual_Correspondence_Humans_as_a_Cue_for_Extreme-View_Geometry_CVPR_2022_paper.html | CVPR 2022 | null |
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way | Qi Jia, Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, Zhongxuan Luo | It is challenging to accurately detect camouflaged objects from their highly similar surroundings. Existing methods mainly leverage a single-stage detection fashion, while neglecting small objects with low-resolution fine edges requires more operations than the larger ones. To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion. Specifically, we design a new discriminative mask which makes the model attend on the fixation and edge regions. In addition, we leverage an attention-based sampler to magnify the object region progressively with no need of enlarging the image size. Extensive experiments show our SegMaR achieves remarkable and consistent improvements over other state-of-the-art methods. Especially, we surpass two competitive methods 7.4% and 20.0% respectively in average over standard evaluation metrics on small camouflaged objects. Additional studies provide more promising insights into SegMaR, including its effectiveness on the discriminative mask and its generalization to other network architectures. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jia_Segment_Magnify_and_Reiterate_Detecting_Camouflaged_Objects_the_Hard_Way_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_Segment_Magnify_and_Reiterate_Detecting_Camouflaged_Objects_the_Hard_Way_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_Segment_Magnify_and_Reiterate_Detecting_Camouflaged_Objects_the_Hard_Way_CVPR_2022_paper.html | CVPR 2022 | null |
SIMBAR: Single Image-Based Scene Relighting for Effective Data Augmentation for Automated Driving Vision Tasks | Xianling Zhang, Nathan Tseng, Ameerah Syed, Rohan Bhasin, Nikita Jaipuria | Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a controllable manner, presents an opportunity to augment datasets with a richer variety of lighting conditions, similar to what would be encountered in the real-world. This paper presents a novel image-based relighting pipeline, SIMBAR, that can work with a single image as input. To the best of our knowledge, there is no prior work on scene relighting leveraging explicit geometric representations from a single image. We present qualitative comparisons with prior multi-view scene relighting baselines. To further validate and effectively quantify the benefit of leveraging SIMBAR for data augmentation for automated driving vision tasks, object detection and tracking experiments are conducted with a state-of-the-art method, a Multiple Object Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained from scratch and tested on Virtual KITTI. For more details and SIMBAR relit datasets, please visit our project website (https://simbarv1.github.io/). | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_SIMBAR_Single_Image-Based_Scene_Relighting_for_Effective_Data_Augmentation_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_SIMBAR_Single_Image-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00644 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_SIMBAR_Single_Image-Based_Scene_Relighting_for_Effective_Data_Augmentation_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_SIMBAR_Single_Image-Based_Scene_Relighting_for_Effective_Data_Augmentation_for_CVPR_2022_paper.html | CVPR 2022 | null |
Shape From Thermal Radiation: Passive Ranging Using Multi-Spectral LWIR Measurements | Yasuto Nagase, Takahiro Kushida, Kenichiro Tanaka, Takuya Funatomi, Yasuhiro Mukaigawa | In this paper, we propose a new cue of depth sensing using thermal radiation. Our method realizes passive, texture independent, far range, and dark scene applicability, which can broaden the depth sensing subjects. A key observation is that thermal radiation is attenuated by the air and is wavelength dependent. By modeling the wavelength-dependent attenuation by the air and building a multi-spectral LWIR measurement system, we can jointly estimate the depth, temperature, and emissivity of the target. We analytically show the capability of the thermal radiation cue and show the effectiveness of the method in real-world scenes using an imaging system with a few bandpass filters. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nagase_Shape_From_Thermal_Radiation_Passive_Ranging_Using_Multi-Spectral_LWIR_Measurements_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nagase_Shape_From_Thermal_Radiation_Passive_Ranging_Using_Multi-Spectral_LWIR_Measurements_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nagase_Shape_From_Thermal_Radiation_Passive_Ranging_Using_Multi-Spectral_LWIR_Measurements_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Label Classification With Partial Annotations Using Class-Aware Selective Loss | Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman, Avi Ben-Cohen, Nadav Zamir, Asaf Noy, Lihi Zelnik-Manor | Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Different methods for handling the missing labels induce different properties on the model and impact its accuracy. In this work, we analyze the partial labeling problem, then propose a solution based on two key ideas. First, un-annotated labels should be treated selectively according to two probability quantities: the class distribution in the overall dataset and the specific label likelihood for a given data sample. We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations. Second, during the training of the target model, we emphasize the contribution of annotated labels over originally un-annotated labels by using a dedicated asymmetric loss. With our novel approach, we achieve state-of-the-art results on OpenImages dataset (e.g. reaching 87.3 mAP on V6). In addition, experiments conducted on LVIS and simulated-COCO demonstrate the effectiveness of our approach. Code is available at https://github.com/Alibaba-MIIL/PartialLabelingCSL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ben-Baruch_Multi-Label_Classification_With_Partial_Annotations_Using_Class-Aware_Selective_Loss_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ben-Baruch_Multi-Label_Classification_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ben-Baruch_Multi-Label_Classification_With_Partial_Annotations_Using_Class-Aware_Selective_Loss_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ben-Baruch_Multi-Label_Classification_With_Partial_Annotations_Using_Class-Aware_Selective_Loss_CVPR_2022_paper.html | CVPR 2022 | null |
HSC4D: Human-Centered 4D Scene Capture in Large-Scale Indoor-Outdoor Space Using Wearable IMUs and LiDAR | Yudi Dai, Yitai Lin, Chenglu Wen, Siqi Shen, Lan Xu, Jingyi Yu, Yuexin Ma, Cheng Wang | We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments. Using only body-mounted IMUs and LiDAR, HSC4D is space-free without any external devices' constraints and map-free without pre-built maps. Considering that IMUs can capture human poses but always drift for long-period use, while LiDAR is stable for global localization but rough for local positions and orientations, HSC4D makes both sensors complement each other by a joint optimization and achieves promising results for long-term capture. Relationships between humans and environments are also explored to make their interaction more realistic. To facilitate many down-stream tasks, like AR, VR, robots, autonomous driving, etc., we propose a dataset containing three large scenes (1k-5k m^2 ) with accurate dynamic human motions and locations. Diverse scenarios (climbing gym, multi-story building, slope, etc.) and challenging human activities (exercising, walking up/down stairs, climbing, etc.) demonstrate the effectiveness and the generalization ability of HSC4D. The dataset and code is available at lidarhumanmotion.net/hsc4d. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_HSC4D_Human-Centered_4D_Scene_Capture_in_Large-Scale_Indoor-Outdoor_Space_Using_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dai_HSC4D_Human-Centered_4D_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.09215 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_HSC4D_Human-Centered_4D_Scene_Capture_in_Large-Scale_Indoor-Outdoor_Space_Using_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_HSC4D_Human-Centered_4D_Scene_Capture_in_Large-Scale_Indoor-Outdoor_Space_Using_CVPR_2022_paper.html | CVPR 2022 | https://openaccess.thecvf.com |
CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings | Zhiwen Fan, Tianlong Chen, Peihao Wang, Zhangyang Wang | Understanding 2D computer-aided design (CAD) drawings plays a crucial role for creating 3D prototypes in architecture, engineering and construction (AEC) industries. The task of automated panoptic symbol spotting, i.e., to spot and parse both countable object instances (windows, doors, tables, etc.) and uncountable stuff (wall, railing, etc.) from CAD drawings, has recently drawn interests from the computer vision community. Unfortunately, the highly irregular ordering and orientations set major roadblocks for this task. Existing methods, based on convolutional neural networks (CNNs) and/or graph neural networks (GNNs), regress instance bounding boxes in the pixel domain and then convert the predictions into symbols. In this paper, we present a novel framework named CADTransformer, that can painlessly modify existing vision transformer (ViT) backbones to tackle the above limitations for the panoptic symbol spotting task. CADTransformer tokenizes directly from the set of graphical primitives in CAD drawings, and correspondingly optimizes line-grained semantic and instance symbol spotting altogether by a pair of prediction heads. The backbone is further enhanced with a few plug-and-play modifications, including a neighborhood aware self-attention, hierarchical feature aggregation, and graphic entity position encoding, to bake in the structure prior while optimizing the efficiency. Besides, a new data augmentation method, termed Random Layer, is proposed by the layer-wise separation and recombination of a CAD drawing. Overall, CADTransformer significantly boosts the previous state-of-the-art from 0.595 to 0.685 in the panoptic quality (PQ) metric, on the recently released FloorPlanCAD dataset. We further demonstrate that our model can spot symbols with irregular shapes and arbitrary orientations. Our codes are available in https://github.com/VITA-Group/CADTransformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fan_CADTransformer_Panoptic_Symbol_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fan_CADTransformer_Panoptic_Symbol_Spotting_Transformer_for_CAD_Drawings_CVPR_2022_paper.html | CVPR 2022 | null |
IntraQ: Learning Synthetic Images With Intra-Class Heterogeneity for Zero-Shot Network Quantization | Yunshan Zhong, Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, Rongrong Ji | Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of the real data. In this paper, we observe an interesting phenomenon of intra-class heterogeneity in real data and show that existing methods fail to retain this property in their synthetic images, which causes a limited performance increase. To address this issue, we propose a novel zero-shot quantization method referred to as IntraQ. First, we propose a local object reinforcement that locates the target objects at different scales and positions of the synthetic images. Second, we introduce a marginal distance constraint to form class-related features distributed in a coarse area. Lastly, we devise a soft inception loss which injects a soft prior label to prevent the synthetic images from being overfitting to a fixed object. Our IntraQ is demonstrated to well retain the intra-class heterogeneity in the synthetic images and also observed to perform state-of-the-art. For example, compared to the advanced ZSQ, our IntraQ obtains 9.17% increase of the top-1 accuracy on ImageNet when all layers of MobileNetV1 are quantized to 4-bit. Code is at https://github.com/zysxmu/IntraQ. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_IntraQ_Learning_Synthetic_Images_With_Intra-Class_Heterogeneity_for_Zero-Shot_Network_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_IntraQ_Learning_Synthetic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09136 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_IntraQ_Learning_Synthetic_Images_With_Intra-Class_Heterogeneity_for_Zero-Shot_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_IntraQ_Learning_Synthetic_Images_With_Intra-Class_Heterogeneity_for_Zero-Shot_Network_CVPR_2022_paper.html | CVPR 2022 | null |
M3L: Language-Based Video Editing via Multi-Modal Multi-Level Transformers | Tsu-Jui Fu, Xin Eric Wang, Scott T. Grafton, Miguel P. Eckstein, William Yang Wang | Video editing tools are widely used nowadays for digital design. Although the demand for these tools is high, the prior knowledge required makes it difficult for novices to get started. Systems that could follow natural language instructions to perform automatic editing would significantly improve accessibility. This paper introduces the language-based video editing (LBVE) task, which allows the model to edit, guided by text instruction, a source video into a target video. LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video; 2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction. We propose a Multi-Modal Multi-Level Transformer (M3L-Transformer) to carry out LBVE. The M3L-Transformer dynamically learns the correspondence between video perception and language semantic at different levels, which benefits both the video understanding and video frame synthesis. We build three new datasets for evaluation, including two diagnostic and one from natural videos with human-labeled text. Extensive experimental results show that M3L-Transformer is effective for video editing and that LBVE can lead to a new field toward vision-and-language research. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fu_M3L_Language-Based_Video_Editing_via_Multi-Modal_Multi-Level_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fu_M3L_Language-Based_Video_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.01122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fu_M3L_Language-Based_Video_Editing_via_Multi-Modal_Multi-Level_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fu_M3L_Language-Based_Video_Editing_via_Multi-Modal_Multi-Level_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
I M Avatar: Implicit Morphable Head Avatars From Videos | Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, Otmar Hilliges | Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details. Neural volumetric representations approach photorealism but are hard to animate and do not generalize well to unseen expressions. To tackle this problem, we propose IMavatar (Implicit Morphable avatar), a novel method for learning implicit head avatars from monocular videos. Inspired by the fine-grained control mechanisms afforded by conventional 3DMMs, we represent the expression- and pose- related deformations via learned blendshapes and skinning fields. These attributes are pose-independent and can be used to morph the canonical geometry and texture fields given novel expression and pose parameters. We employ ray marching and iterative root-finding to locate the canonical surface intersection for each pixel. A key contribution is our novel analytical gradient formulation that enables end- to-end training of IMavatars from videos. We show quantitatively and qualitatively that our method improves geometry and covers a more complete expression space compared to state-of-the-art methods. Code and data can be found at https://ait.ethz.ch/projects/2022/IMavatar/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_I_M_Avatar_Implicit_Morphable_Head_Avatars_From_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_I_M_Avatar_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_I_M_Avatar_Implicit_Morphable_Head_Avatars_From_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_I_M_Avatar_Implicit_Morphable_Head_Avatars_From_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
BodyMap: Learning Full-Body Dense Correspondence Map | Anastasia Ianina, Nikolaos Sarafianos, Yuanlu Xu, Ignacio Rocco, Tony Tung | Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction. In this paper we present BodyMap, a new framework for obtaining high-definition full-body and continuous dense correspondence between in-the-wild images of clothed humans and the surface of a 3D template model. The correspondences cover fine details such as hands and hair, while capturing regions far from the body surface, such as loose clothing. Prior methods for estimating such dense surface correspondence i) cut a 3D body into parts which are unwrapped to a 2D UV space, producing discontinuities along part seams, or ii) use a single surface for representing the whole body, but none handled body details. Here, we introduce a novel network architecture with Vision Transformers that learn fine-level features on a continuous body surface. BodyMap outperforms prior work on various metrics and datasets, including DensePose-COCO by a large margin. Furthermore, we show various applications ranging from multi-layer dense cloth correspondence, neural rendering with novel-view synthesis and appearance swapping. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ianina_BodyMap_Learning_Full-Body_Dense_Correspondence_Map_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ianina_BodyMap_Learning_Full-Body_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.09111 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ianina_BodyMap_Learning_Full-Body_Dense_Correspondence_Map_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ianina_BodyMap_Learning_Full-Body_Dense_Correspondence_Map_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly-Supervised Metric Learning With Cross-Module Communications for the Classification of Anterior Chamber Angle Images | Jingqi Huang, Yue Ning, Dong Nie, Linan Guan, Xiping Jia | As the basis for developing glaucoma treatment strategies, Anterior Chamber Angle (ACA) evaluation is usually dependent on experts' judgements. However, experienced ophthalmologists needed for these judgements are not widely available. Thus, computer-aided ACA evaluations become a pressing and efficient solution for this issue. In this paper, we propose a novel end-to-end framework GCNet for automated Glaucoma Classification based on ACA images or other Glaucoma-related medical images. We first collect and label an ACA image dataset with some pixel-level annotations. Next, we introduce a segmentation module and an embedding module to enhance the performance of classifying ACA images. Within GCNet, we design a Cross-Module Aggregation Net (CMANet) which is a weakly-supervised metric learning network to capture contextual information exchanging across these modules. We conduct experiments on the ACA dataset and two public datasets REFUGE and SIGF. Our experimental results demonstrate that GCNet outperforms several state-of-the-art deep models in the tasks of glaucoma medical image classifications. The source code of GCNet can be found at https://github.com/Jingqi-H/GCNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Weakly-Supervised_Metric_Learning_With_Cross-Module_Communications_for_the_Classification_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Weakly-Supervised_Metric_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Weakly-Supervised_Metric_Learning_With_Cross-Module_Communications_for_the_Classification_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Weakly-Supervised_Metric_Learning_With_Cross-Module_Communications_for_the_Classification_of_CVPR_2022_paper.html | CVPR 2022 | null |
A Hybrid Egocentric Activity Anticipation Framework via Memory-Augmented Recurrent and One-Shot Representation Forecasting | Tianshan Liu, Kin-Man Lam | Egocentric activity anticipation involves identifying the interacted objects and target action patterns in the near future. A standard activity anticipation paradigm is recurrently forecasting future representations to compensate the missing activity semantics of the unobserved sequence. However, the limitations of current recursive prediction models arise from two aspects: (i) The vanilla recurrent units are prone to accumulated errors in relatively long periods of anticipation. (ii) The anticipated representations may be insufficient to reflect the desired semantics of the target activity, due to lack of contextual clues. To address these issues, we propose "HRO", a hybrid framework that integrates both the memory-augmented recurrent and one-shot representation forecasting strategies. Specifically, to solve the limitation (i), we introduce a memory-augmented contrastive learning paradigm to regulate the process of the recurrent representation forecasting. Since the external memory bank maintains long-term prototypical activity semantics, it can guarantee that the anticipated representations are reconstructed from the discriminative activity prototypes. To further guide the learning of the memory bank, two auxiliary loss functions are designed, based on the diversity and sparsity mechanisms, respectively. Furthermore, to resolve the limitation (ii), a one-shot transferring paradigm is proposed to enrich the forecasted representations, by distilling the holistic activity semantics after the target anticipation moment, in the offline training. Extensive experimental results on two large-scale data sets validate the effectiveness of our proposed HRO method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_A_Hybrid_Egocentric_Activity_Anticipation_Framework_via_Memory-Augmented_Recurrent_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_A_Hybrid_Egocentric_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_A_Hybrid_Egocentric_Activity_Anticipation_Framework_via_Memory-Augmented_Recurrent_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_A_Hybrid_Egocentric_Activity_Anticipation_Framework_via_Memory-Augmented_Recurrent_and_CVPR_2022_paper.html | CVPR 2022 | null |
It's All in the Teacher: Zero-Shot Quantization Brought Closer to the Teacher | Kanghyun Choi, Hye Yoon Lee, Deokki Hong, Joonsang Yu, Noseong Park, Youngsok Kim, Jinho Lee | Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks. In real-world environments, however, such a method is frequently infeasible because training data is unavailable due to security, privacy, or confidentiality concerns. Zero-shot quantization addresses such problems, usually by taking information from the weights of a full-precision teacher network to compensate the performance drop of the quantized networks. In this paper, we first analyze the loss surface of state-of-the-art zero-shot quantization techniques and provide several findings. In contrast to usual knowledge distillation problems, zero-shot quantization often suffers from 1) the difficulty of optimizing multiple loss terms together, and 2) the poor generalization capability due to the use of synthetic samples. Furthermore, we observe that many weights fail to cross the rounding threshold during training the quantized networks even when it is necessary to do so for better performance. Based on the observations, we propose AIT, a simple yet powerful technique for zero-shot quantization, which addresses the aforementioned two problems in the following way: AIT i) uses a KL distance loss only without a cross-entropy loss, and ii) manipulates gradients to guarantee that a certain portion of weights are properly updated after crossing the rounding thresholds. Experiments show that AIT outperforms the performance of many existing methods by a great margin, taking over the overall state-of-the-art position in the field. | https://openaccess.thecvf.com/content/CVPR2022/papers/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Choi_Its_All_in_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Its_All_in_the_Teacher_Zero-Shot_Quantization_Brought_Closer_to_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Segmentation of the Inferior Alveolar Nerve Through Deep Label Propagation | Marco Cipriano, Stefano Allegretti, Federico Bolelli, Federico Pollastri, Costantino Grana | Many recent works in dentistry and maxillofacial imagery focused on the Inferior Alveolar Nerve (IAN) canal detection. Unfortunately, the small extent of available 3D maxillofacial datasets has strongly limited the performance of deep learning-based techniques. On the other hand, a huge amount of sparsely annotated data is produced every day from the regular procedures in the maxillofacial practice. Despite the amount of sparsely labeled images being significant, the adoption of those data still raises an open problem. Indeed, the deep learning approach frames the presence of dense annotations as a crucial factor. Recent efforts in literature have hence focused on developing label propagation techniques to expand sparse annotations into dense labels.However, the proposed methods proved only marginally effective for the purpose of segmenting the alveolar nerve in CBCT scans.This paper exploits and publicly releases a new 3D densely annotated dataset, through which we are able to train a deep label propagation model which obtains better results than those available in literature. By combining a segmentation model trained on the 3D annotated data and label propagation, we significantly improve the state of the art in the Inferior Alveolar Nerve segmentation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cipriano_Improving_Segmentation_of_the_Inferior_Alveolar_Nerve_Through_Deep_Label_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cipriano_Improving_Segmentation_of_the_Inferior_Alveolar_Nerve_Through_Deep_Label_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cipriano_Improving_Segmentation_of_the_Inferior_Alveolar_Nerve_Through_Deep_Label_CVPR_2022_paper.html | CVPR 2022 | null |
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-Resolution | Jianqi Ma, Zhetong Liang, Lei Zhang | Scene text image super-resolution aims to increase the resolution and readability of the text in low-resolution images. Though significant improvement has been achieved by deep convolutional neural networks (CNNs), it remains difficult to reconstruct high-resolution images for spatially deformed texts, especially rotated and curve-shaped ones. This is because the current CNN-based methods adopt locality-based operations, which are not effective to deal with the variation caused by deformations. In this paper, we propose a CNN based Text ATTention network (TATT) to address this problem. The semantics of the text are firstly extracted by a text recognition module as text prior information. Then we design a novel transformer-based module, which leverages global attention mechanism, to exert the semantic guidance of text prior to the text reconstruction process. In addition, we propose a text structure consistency loss to refine the visual appearance by imposing structural consistency on the reconstructions of regular and deformed texts. Experiments on the benchmark TextZoom dataset show that the proposed TATT not only achieves state-of-the-art performance in terms of PSNR/SSIM metrics, but also significantly improves the recognition accuracy in the downstream text recognition task, particularly for text instances with multi-orientation and curved shapes. Code is available at https://github.com/mjq11302010044/TATT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_A_Text_Attention_Network_for_Spatial_Deformation_Robust_Scene_Text_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_A_Text_Attention_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09388 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_A_Text_Attention_Network_for_Spatial_Deformation_Robust_Scene_Text_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_A_Text_Attention_Network_for_Spatial_Deformation_Robust_Scene_Text_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Modal Dynamic Graph Transformer for Visual Grounding | Sijia Chen, Baochun Li | Visual grounding (VG) aims to align the correct regions of an image with a natural language query about that image. We found that existing VG methods are trapped by the single-stage grounding process that performs a sole evaluate-and- rank for meticulously prepared regions. Their performance depends on the density and quality of the candidate regions and is capped by the inability to optimize the located regions continuously. To address these issues, we propose to remodel VG into a progressively optimized visual semantic alignment process. Our proposed multi-modal dynamic graph transformer (M-DGT) achieves this by building upon the dynamic graph structure with regions as nodes and their semantic relations as edges. Starting from a few randomly initialized regions, M-DGT is able to make sustainable adjustments (i.e., 2D spatial transformation and deletion) to the nodes and edges of the graph based on multi-modal information and the graph feature, thereby efficiently shrinking the graph to approach the ground truth regions. Experiments show that with an average of 48 boxes as initialization, the performance of M-DGT on the Flickr30K entity and RefCOCO dataset outperforms existing state-of-the-art methods by a substantial margin in terms of both the accuracy and Intersect over Union (IOU) scores. Furthermore, introducing M-DGT to optimize the predicted regions of existing methods can further significantly improve their performance. The source codes are available at https://github.com/iQua/M-DGT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Multi-Modal_Dynamic_Graph_Transformer_for_Visual_Grounding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Multi-Modal_Dynamic_Graph_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Multi-Modal_Dynamic_Graph_Transformer_for_Visual_Grounding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Multi-Modal_Dynamic_Graph_Transformer_for_Visual_Grounding_CVPR_2022_paper.html | CVPR 2022 | null |
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework | Ivan Shugurov, Fu Li, Benjamin Busam, Slobodan Ilic | We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects. At test time, it takes as input a target image and a textured 3D query model. The core idea is to represent a 3D model with a number of 2D templates rendered from different viewpoints. This enables CNN-based direct dense feature extraction and matching. The object is first localized in 2D, then its approximate viewpoint is estimated, followed by dense 2D-3D correspondence prediction. The final pose is computed with PnP. We evaluate the method on LineMOD, Occlusion, Homebrewed, YCB-V and TLESS datasets and report very competitive performance in comparison to the state-of-the-art methods trained on synthetic data, even though our method is not trained on the object models used for testing. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shugurov_OSOP_A_Multi-Stage_One_Shot_Object_Pose_Estimation_Framework_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shugurov_OSOP_A_Multi-Stage_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15533 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shugurov_OSOP_A_Multi-Stage_One_Shot_Object_Pose_Estimation_Framework_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shugurov_OSOP_A_Multi-Stage_One_Shot_Object_Pose_Estimation_Framework_CVPR_2022_paper.html | CVPR 2022 | null |
Generative Cooperative Learning for Unsupervised Video Anomaly Detection | M. Zaigham Zaheer, Arif Mahmood, M. Haris Khan, Mattia Segu, Fisher Yu, Seung-Ik Lee | Video anomaly detection is well investigated in weakly supervised and one-class classification (OCC) settings. However, unsupervised video anomaly detection is quite sparse, likely because anomalies are less frequent in occurrence and usually not well-defined, which when coupled with the absence of ground truth supervision, could adversely affect the convergence of learning algorithms. This problem is challenging yet rewarding as it can completely eradicate the costs of obtaining laborious annotations and enable such systems to be deployed without human intervention. To this end, we propose a novel unsupervised Generative Cooperative Learning (GCL) approach for video anomaly detection that exploits the low frequency of anomalies towards building a cross-supervision between a generator and a discriminator. In essence, both networks get trained in a cooperative fashion, thereby facilitating the overall convergence. We conduct extensive experiments on two large-scale video anomaly detection datasets, UCF crime and ShanghaiTech. Consistent improvement over the existing state-of-the-art unsupervised and OCC methods corroborate the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zaheer_Generative_Cooperative_Learning_for_Unsupervised_Video_Anomaly_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zaheer_Generative_Cooperative_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03962 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zaheer_Generative_Cooperative_Learning_for_Unsupervised_Video_Anomaly_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zaheer_Generative_Cooperative_Learning_for_Unsupervised_Video_Anomaly_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Semantic Segmentation: A Prototype View | Tianfei Zhou, Wenguan Wang, Ender Konukoglu, Luc Van Gool | Prevalent semantic segmentation solutions, despite their different network designs (FCN based or attention based) and mask decoding strategies (parametric softmax based or pixel-query based), can be placed in one category, by considering the softmax weights or query vectors as learnable class prototypes. In light of this prototype view, this study uncovers several limitations of such parametric segmentation regime, and proposes a nonparametric alternative based on non-learnable prototypes. Instead of prior methods learning a single weight/query vector for each class in a fully parametric manner, our model represents each class as a set of non-learnable prototypes, relying solely on the mean features of several training pixels within that class. The dense prediction is thus achieved by nonparametric nearest prototype retrieving. This allows our model to directly shape the pixel embedding space, by optimizing the arrangement between embedded pixels and anchored prototypes. It is able to handle arbitrary number of classes with a constant amount of learnable parameters.We empirically show that, with FCN based and attention based segmentation models (i.e., HRNet, Swin, SegFormer) and backbones (i.e., ResNet, HRNet, Swin, MiT), our nonparametric framework yields compelling results over several datasets (i.e., ADE20K, Cityscapes, COCO-Stuff), and performs well in the large-vocabulary situation. We expect this work will provoke a rethink of the current de facto semantic segmentation model design. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Rethinking_Semantic_Segmentation_A_Prototype_View_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Rethinking_Semantic_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15102 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Rethinking_Semantic_Segmentation_A_Prototype_View_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Rethinking_Semantic_Segmentation_A_Prototype_View_CVPR_2022_paper.html | CVPR 2022 | null |
Geometric Transformer for Fast and Robust Point Cloud Registration | Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Kai Xu | We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it robust in low-overlap cases and invariant to rigid transformation. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to 100 times acceleration. Our method improves the inlier ratio by 17 30 percentage points and the registration recall by over 7 points on the challenging 3DLoMatch benchmark. Our code and models are available at https://github.com/qinzheng93/GeoTransformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qin_Geometric_Transformer_for_Fast_and_Robust_Point_Cloud_Registration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qin_Geometric_Transformer_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.06688 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_Geometric_Transformer_for_Fast_and_Robust_Point_Cloud_Registration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qin_Geometric_Transformer_for_Fast_and_Robust_Point_Cloud_Registration_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition | Yinghao Xu, Fangyun Wei, Xiao Sun, Ceyuan Yang, Yujun Shen, Bo Dai, Bolei Zhou, Stephen Lin | Semi-supervised action recognition is a challenging but important task due to the high cost of data annotation. A common approach to this problem is to assign unlabeled data with pseudo-labels, which are then used as additional supervision in training. Typically in recent work, the pseudo-labels are obtained by training a model on the labeled data, and then using confident predictions from the model to teach itself.In this work, we propose a more effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL). Concretely, we introduce a lightweight auxiliary network in addition to the primary backbone, and ask them to predict pseudo-labels for each other. We observe that, due to their different structural biases, these two models tend to learn complementary representations from the same video clips. Each model can thus benefit from its counterpart by utilizing cross-model predictions as supervision. Experiments on different data partition protocols demonstrate the significant improvement of our framework over existing alternatives. For example, CMPL achieves 7.6% and 25.1% Top-1 accuracy on Kinetics-400 and UCF-101 using only the RGB modality and 1% labeled data, outperforming our baseline model, FixMatch, by 9.0% and 10.3%, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Cross-Model_Pseudo-Labeling_for_Semi-Supervised_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Cross-Model_Pseudo-Labeling_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Cross-Model_Pseudo-Labeling_for_Semi-Supervised_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Cross-Model_Pseudo-Labeling_for_Semi-Supervised_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
UMT: Unified Multi-Modal Transformers for Joint Video Moment Retrieval and Highlight Detection | Ye Liu, Siyuan Li, Yang Wu, Chang-Wen Chen, Ying Shan, Xiaohu Qie | Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era. Nevertheless, jointly conducting moment retrieval and highlight detection is an emerging research topic, even though its component problems and some related tasks have already been studied for a while. In this paper, we present the first unified framework, named Unified Multi-modal Transformers (UMT), capable of realizing such joint optimization while can also be easily degenerated for solving individual problems. As far as we are aware, this is the first scheme to integrate multi-modal (visual-audio) learning for either joint optimization or the individual moment retrieval task, and tackles moment retrieval as a keypoint detection problem using a novel query generator and query decoder. Extensive comparisons with existing methods and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and TVSum datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method under various settings. Source code and pre-trained models are available at https://github.com/TencentARC/UMT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_UMT_Unified_Multi-Modal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12745 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.html | CVPR 2022 | null |
Dual-Shutter Optical Vibration Sensing | Mark Sheinin, Dorian Chan, Matthew O'Toole, Srinivasa G. Narasimhan | Visual vibrometry is a highly useful tool for remote capture of audio, as well as the physical properties of materials, human heart rate, and more. While visually-observable vibrations can be captured directly with a high-speed camera, minute imperceptible object vibrations can be optically amplified by imaging the displacement of a speckle pattern, created by shining a laser beam on the vibrating surface. In this paper, we propose a novel method for sensing vibrations at high speeds (up to 63kHz), for multiple scene sources at once, using sensors rated for only 130Hz operation. Our method relies on simultaneously capturing the scene with two cameras equipped with rolling and global shutter sensors, respectively. The rolling shutter camera captures distorted speckle images that encode the highspeed object vibrations. The global shutter camera captures undistorted reference images of the speckle pattern, helping to decode the source vibrations. We demonstrate our method by capturing vibration caused by audio sources (e.g. speakers, human voice, and musical instruments) and analyzing the vibration modes of a tuning fork. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sheinin_Dual-Shutter_Optical_Vibration_Sensing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sheinin_Dual-Shutter_Optical_Vibration_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sheinin_Dual-Shutter_Optical_Vibration_Sensing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sheinin_Dual-Shutter_Optical_Vibration_Sensing_CVPR_2022_paper.html | CVPR 2022 | null |
Demystifying the Neural Tangent Kernel From a Practical Perspective: Can It Be Trusted for Neural Architecture Search Without Training? | Jisoo Mok, Byunggook Na, Ji-Hoon Kim, Dongyoon Han, Sungroh Yoon | In Neural Architecture Search (NAS), reducing the cost of architecture evaluation remains one of the most crucial challenges. Among a plethora of efforts to bypass training of each candidate architecture to convergence for evaluation, the Neural Tangent Kernel (NTK) is emerging as a promising theoretical framework that can be utilized to estimate the performance of a neural architecture at initialization. In this work, we revisit several at-initialization metrics that can be derived from the NTK and reveal their key shortcomings. Then, through the empirical analysis of the time evolution of NTK, we deduce that modern neural architectures exhibit highly non-linear characteristics, making the NTK-based metrics incapable of reliably estimating the performance of an architecture without some amount of training. To take such non-linear characteristics into account, we introduce Label-Gradient Alignment (LGA), a novel NTK-based metric whose inherent formulation allows it to capture the large amount of non-linear advantage present in modern neural architectures. With minimal amount of training, LGA obtains a meaningful level of rank correlation with the post-training test accuracy of an architecture. Lastly, we demonstrate that LGA, complemented with few epochs of training, successfully guides existing search algorithms to achieve competitive search performances with significantly less search cost. The code is available at: https://github.com/nutellamok/DemystifyingNTK. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mok_Demystifying_the_Neural_Tangent_Kernel_From_a_Practical_Perspective_Can_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mok_Demystifying_the_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14577 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mok_Demystifying_the_Neural_Tangent_Kernel_From_a_Practical_Perspective_Can_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mok_Demystifying_the_Neural_Tangent_Kernel_From_a_Practical_Perspective_Can_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Find Good Models in RANSAC | Daniel Barath, Luca Cavalli, Marc Pollefeys | We propose the Model Quality Network, MQ-Net in short, for predicting the quality, e.g. the pose error of essential matrices, of models generated inside RANSAC. It replaces the traditionally used scoring techniques, e.g., inlier counting of RANSAC, truncated loss of MSAC, and the marginalization-based loss of MAGSAC++. Moreover, Minimal samples Filtering Network (MF-Net) is proposed for the early rejection of minimal samples that likely lead to degenerate models or to ones that are inconsistent with the scene geometry, e.g., due to the chirality constraint. We show on 54450 image pairs from public real-world datasets that the proposed MQ-Net leads to results superior to the state-of-the-art in terms of accuracy by a large margin. The proposed MF-Net accelerates the fundamental matrix estimation by five times and significantly reduces the essential matrix estimation time while slightly improving accuracy as well. Also, we show experimentally that consensus maximization, i.e. inlier counting, is not an inherently good measure of the model quality for relative pose estimation. The code and models will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Barath_Learning_To_Find_Good_Models_in_RANSAC_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Barath_Learning_To_Find_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Barath_Learning_To_Find_Good_Models_in_RANSAC_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Barath_Learning_To_Find_Good_Models_in_RANSAC_CVPR_2022_paper.html | CVPR 2022 | null |
Interactiveness Field in Human-Object Interactions | Xinpeng Liu, Yong-Lu Li, Xiaoqian Wu, Yu-Wing Tai, Cewu Lu, Chi-Keung Tang | Human-Object Interaction (HOI) detection plays a core role in activity understanding. Though recent two/one-stage methods have achieved impressive results, as an essential step, discovering interactive human-object pairs remains challenging. Both one/two-stage methods fail to effectively extract interactive pairs instead of generating redundant negative pairs. In this work, we introduce a previously overlooked interactiveness bimodal prior: given an object in an image, after pairing it with the humans, the generated pairs are either mostly non-interactive, or mostly interactive, with the former more frequent than the latter. Based on this interactiveness bimodal prior we propose the "interactiveness field". To make the learned field compatible with real HOI image considerations, we propose new energy constraints based on the cardinality and difference in the inherent "interactiveness field" underlying interactive versus non-interactive pairs. Consequently, our method can detect more precise pairs and thus significantly boost HOI detection performance, which is validated on widely-used benchmarks where we achieve decent improvements overstate-of-the-arts. Our code will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Interactiveness_Field_in_Human-Object_Interactions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Interactiveness_Field_in_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07718 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Interactiveness_Field_in_Human-Object_Interactions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Interactiveness_Field_in_Human-Object_Interactions_CVPR_2022_paper.html | CVPR 2022 | null |
BodyGAN: General-Purpose Controllable Neural Human Body Generation | Chaojie Yang, Hanhui Li, Shengjie Wu, Shengkai Zhang, Haonan Yan, Nianhong Jiao, Jie Tang, Runnan Zhou, Xiaodan Liang, Tianxiang Zheng | Recent advances in generative adversarial networks (GANs) have provided potential solutions for photorealistic human image synthesis. However, the explicit and individual control of synthesis over multiple factors, such as poses, body shapes, and skin colors, remains difficult for existing methods. This is because current methods mainly rely on a single pose/appearance model, which is limited in disentangling various poses and appearance in human images. In addition, such a unimodal strategy is prone to causing severe artifacts in the generated images like color distortions and unrealistic textures. To tackle these issues, this paper proposes a multi-factor conditioned method dubbed BodyGAN. Specifically, given a source image, our Body-GAN aims at capturing the characteristics of the human body from multiple aspects: (i) A pose encoding branch consisting of three hybrid subnetworks is adopted, to generate the semantic segmentation based representation, the 3D surface based representation, and the key point based representation of the human body, respectively. (ii) Based on the segmentation results, an appearance encoding branch is used to obtain the appearance information of the human body parts. (iii) The outputs of these two branches are represented by user-editable condition maps, which are then processed by a generator to predict the synthesized image. In this way, our BodyGAN can achieve the fine-grained disentanglement of pose, body shape, and appearance, and consequently enable the explicit and effective control of synthesis with diverse conditions. Extensive experiments on multiple datasets and a comprehensive user-study show that our BodyGAN achieves the state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_BodyGAN_General-Purpose_Controllable_Neural_Human_Body_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_BodyGAN_General-Purpose_Controllable_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_BodyGAN_General-Purpose_Controllable_Neural_Human_Body_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_BodyGAN_General-Purpose_Controllable_Neural_Human_Body_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Image Disentanglement Autoencoder for Steganography Without Embedding | Xiyao Liu, Ziping Ma, Junxing Ma, Jian Zhang, Gerald Schaefer, Hui Fang | Conventional steganography approaches embed a secret message into a carrier for concealed communication but are prone to attack by recent advanced steganalysis tools. In this paper, we propose Image DisEntanglement Autoencoder for Steganography (IDEAS) as a novel steganography without embedding (SWE) technique. Instead of directly embedding the secret message into a carrier image, our approach hides it by transforming it into a synthesised image, and is thus fundamentally immune to typical steganalysis attacks. By disentangling an image into two representations for structure and texture, we exploit the stability of structure representation to improve secret message extraction while increasing synthesis diversity via randomising texture representations to enhance steganography security. In addition, we design an adaptive mapping mechanism to further enhance the diversity of synthesised images when ensuring different required extraction levels. Experimental results convincingly demonstrate IDEAS to achieve superior performance in terms of enhanced security, reliable secret message extraction and flexible adaptation for different extraction levels, compared to state-of-the-art SWE methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Image_Disentanglement_Autoencoder_for_Steganography_Without_Embedding_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Image_Disentanglement_Autoencoder_for_Steganography_Without_Embedding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Image_Disentanglement_Autoencoder_for_Steganography_Without_Embedding_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Dense Consistency Regularization for Image-to-Image Translation | Minsu Ko, Eunju Cha, Sungjoo Suh, Huijin Lee, Jae-Joon Han, Jinwoo Shin, Bohyung Han | Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). In this paper, we present a simple but effective regularization technique for improving GAN-based image-to-image translation. To generate images with realistic local semantics and structures, we suggest to use an auxiliary self-supervised loss, enforcing point-wise consistency of the overlapped region between a pair of patches cropped from a single real image during training discriminators of GAN. Our experiment shows that the dense consistency regularization improves performance substantially on various image-to-image translation scenarios. It also achieves extra performance gains by using jointly with recent instance-level regularization methods. Furthermore, we verify that the proposed model captures domain-specific characteristics more effectively with only small fraction of training data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ko_Self-Supervised_Dense_Consistency_Regularization_for_Image-to-Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ko_Self-Supervised_Dense_Consistency_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ko_Self-Supervised_Dense_Consistency_Regularization_for_Image-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ko_Self-Supervised_Dense_Consistency_Regularization_for_Image-to-Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
The Devil Is in the Details: Window-Based Attention for Image Compression | Renjie Zou, Chunfeng Song, Zhaoxiang Zhang | Learned image compression methods have exhibited superior rate-distortion performance than classical image compression standards. Most existing learned image compression models are based on Convolutional Neural Networks (CNNs). Despite great contributions, a main drawback of CNN based model is that its structure is not designed for capturing local redundancy, especially the non-repetitive textures, which severely affects the reconstruction quality. Therefore, how to make full use of both global structure and local texture becomes the core problem for learning-based image compression. Inspired by recent progresses of Vision Transformer (ViT) and Swin Transformer, we found that combining the local-aware attention mechanism with the global-related feature learning could meet the expectation in image compression. In this paper, we first extensively study the effects of multiple kinds of attention mechanisms for local features learning, then introduce a more straightforward yet effective window-based local attention block. The proposed window-based attention is very flexible which could work as a plug-and-play component to enhance CNN and Transformer models. Moreover, we propose a novel Symmetrical TransFormer (STF) framework with absolute transformer blocks in the down-sampling encoder and up-sampling decoder. Extensive experimental evaluations have shown that the proposed method is effective and outperforms the state-of-the-art methods. The code is publicly available at https://github.com/Googolxx/STF. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zou_The_Devil_Is_in_the_Details_Window-Based_Attention_for_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zou_The_Devil_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08450 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_The_Devil_Is_in_the_Details_Window-Based_Attention_for_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_The_Devil_Is_in_the_Details_Window-Based_Attention_for_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Category-Aware Transformer Network for Better Human-Object Interaction Detection | Leizhen Dong, Zhimin Li, Kunlun Xu, Zhijun Zhang, Luxin Yan, Sheng Zhong, Xu Zou | Human-Object Interactions (HOI) detection, which aims to localize a human and a relevant object while recognizing their interaction, is crucial for understanding a still image. Recently, tranformer-based models have significantly advanced the progress of HOI detection. However, the capability of these models has not been fully explored since the Object Query of the model is always simply initialized as just zeros, which would affect the performance. In this paper, we try to study the issue of promoting transformerbased HOI detectors by initializing the Object Query with category-aware semantic information. To this end, we innovatively propose the Category-Aware Transformer Network (CATN). Specifically, the Object Query would be initialized via category priors represented by an external object detection model to yield a better performance. Moreover, such category priors can be further used for enhancing the representation ability of features via the attention mechanism. We have firstly verified our idea via the Oracle experiment by initializing the Object Query with the groundtruth category information. And then extensive experiments have been conducted to show that a HOI detection model equipped with our idea outperforms the baseline by a large margin to achieve a new state-of-the-art result. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Category-Aware_Transformer_Network_for_Better_Human-Object_Interaction_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Category-Aware_Transformer_Network_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04911 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Category-Aware_Transformer_Network_for_Better_Human-Object_Interaction_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Category-Aware_Transformer_Network_for_Better_Human-Object_Interaction_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Depth From Focus With Differential Focus Volume | Fengting Yang, Xiaolei Huang, Zihan Zhou | Depth-from-focus (DFF) is a technique that infers depth using the focus change of a camera. In this work, we propose a convolutional neural network (CNN) to find the best-focused pixels in a focal stack and infer depth from the focus estimation. The key innovation of the network is the novel deep differential focus volume (DFV). By computing the first-order derivative with the stacked features over different focal distances, DFV is able to capture both the focus and context information for focus analysis. Besides, we also introduce a probability regression mechanism for focus estimation to handle sparsely sampled focal stacks and provide uncertainty estimation to the final prediction. Comprehensive experiments demonstrate that the proposed model achieves state-of-the-art performance on multiple datasets with good generalizability and fast speed. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Deep_Depth_From_Focus_With_Differential_Focus_Volume_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Deep_Depth_From_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01712 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Deep_Depth_From_Focus_With_Differential_Focus_Volume_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Deep_Depth_From_Focus_With_Differential_Focus_Volume_CVPR_2022_paper.html | CVPR 2022 | null |
DiLiGenT102: A Photometric Stereo Benchmark Dataset With Controlled Shape and Material Variation | Jieji Ren, Feishi Wang, Jiahao Zhang, Qian Zheng, Mingjun Ren, Boxin Shi | Evaluating photometric stereo using real-world dataset is important yet difficult. Existing datasets are insufficient due to their limited scale and random distributions in shape and material. This paper presents a new real-world photometric stereo dataset with "ground truth" normal maps, which is 10 times larger than the widely adopted one. More importantly, we propose to control the shape and material variations by fabricating objects from CAD models with carefully selected materials, covering typical aspects of reflectance properties that are distinctive for evaluating photometric stereo methods. By benchmarking recent photometric stereo methods using these 100 sets of images, with a special focus on recent learning based solutions, a 10 x 10 shape-material error distribution matrix is visualized to depict a "portrait" for each evaluated method. From such comprehensive analysis, open problems in this field are discussed. To inspire future research, this dataset is available at https://photometricstereo.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_DiLiGenT102_A_Photometric_Stereo_Benchmark_Dataset_With_Controlled_Shape_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_DiLiGenT102_A_Photometric_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_DiLiGenT102_A_Photometric_Stereo_Benchmark_Dataset_With_Controlled_Shape_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_DiLiGenT102_A_Photometric_Stereo_Benchmark_Dataset_With_Controlled_Shape_and_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Fine-Tuning of Zero-Shot Models | Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt | Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-shot and fine-tuned models (WiSE-FT). Compared to standard fine-tuning, WiSE-FT provides large accuracy improvements under distribution shift, while preserving high accuracy on the target distribution. On ImageNet and five derived distribution shifts, WiSE-FT improves accuracy under distribution shift by 4 to 6 percentage points (pp) over prior work while increasing ImageNet accuracy by 1.6 pp. WiSE-FT achieves similarly large robustness gains (2 to 23 pp) on a diverse set of six further distribution shifts, and accuracy gains of 0.8 to 3.3 pp compared to standard fine-tuning on commonly used transfer learning datasets. These improvements come at no additional computational cost during fine-tuning or inference. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wortsman_Robust_Fine-Tuning_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.01903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wortsman_Robust_Fine-Tuning_of_Zero-Shot_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Data-Free Model Stealing in a Hard Label Setting | Sunandini Sanyal, Sravanti Addepalli, R. Venkatesh Babu | Machine learning models deployed as a service (MLaaS) are susceptible to model stealing attacks, where an adversary attempts to steal the model within a restricted access framework. While existing attacks demonstrate near-perfect clone-model performance using softmax predictions of the classification network, most of the APIs allow access to only the top-1 labels. In this work, we show that it is indeed possible to steal Machine Learning models by accessing only top-1 predictions (Hard Label setting) as well, without access to model gradients (Black-Box setting) or even the training dataset (Data-Free setting) within a low query budget. We propose a novel GAN-based framework that trains the student and generator in tandem to steal the model effectively while overcoming the challenge of the hard label setting by utilizing gradients of the clone network as a proxy to the victim's gradients. We propose to overcome the large query costs associated with a typical Data-Free setting by utilizing publicly available (potentially unrelated) datasets as a weak image prior. We additionally show that even in the absence of such data, it is possible to achieve state-of-the-art results within a low query budget using synthetically crafted samples. We are the first to demonstrate the scalability of Model Stealing in a restricted access setting on a 100 class dataset as well. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sanyal_Towards_Data-Free_Model_Stealing_in_a_Hard_Label_Setting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sanyal_Towards_Data-Free_Model_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.11022 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sanyal_Towards_Data-Free_Model_Stealing_in_a_Hard_Label_Setting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sanyal_Towards_Data-Free_Model_Stealing_in_a_Hard_Label_Setting_CVPR_2022_paper.html | CVPR 2022 | null |
PolyWorld: Polygonal Building Extraction With Graph Neural Networks in Satellite Images | Stefano Zorzi, Shabab Bazrafkan, Stefan Habenschuss, Friedrich Fraundorfer | While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of extracted objects instead of rasterized output. This paper introduces PolyWorld, a neural network that directly extracts building vertices from an image and connects them correctly to create precise polygons. The model predicts the connection strength between each pair of vertices using a graph neural network and estimates the assignments by solving a differentiable optimal transport problem. Moreover, the vertex positions are optimized by minimizing a combined segmentation and polygonal angle difference loss. PolyWorld significantly outperforms the state of the art in building polygonization and achieves not only notable quantitative results, but also produces visually pleasing building polygons. Code and trained weights are publicly available at https://github.com/zorzi-s/PolyWorldPretrainedNetwork. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zorzi_PolyWorld_Polygonal_Building_Extraction_With_Graph_Neural_Networks_in_Satellite_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zorzi_PolyWorld_Polygonal_Building_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zorzi_PolyWorld_Polygonal_Building_Extraction_With_Graph_Neural_Networks_in_Satellite_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zorzi_PolyWorld_Polygonal_Building_Extraction_With_Graph_Neural_Networks_in_Satellite_CVPR_2022_paper.html | CVPR 2022 | null |
GAT-CADNet: Graph Attention Network for Panoptic Symbol Spotting in CAD Drawings | Zhaohua Zheng, Jianfang Li, Lingjie Zhu, Honghua Li, Frank Petzold, Ping Tan | Spotting graphical symbols from the computer-aided design (CAD) drawings is essential to many industrial applications. Different from raster images, CAD drawings are vector graphics consisting of geometric primitives such as segments, arcs, and circles. By treating each CAD drawing as a graph, we propose a novel graph attention network GAT-CADNet to solve the panoptic symbol spotting problem: vertex features derived from the GAT branch are mapped to semantic labels, while their attention scores are cascaded and mapped to instance prediction. Our key contributions are three-fold: 1) the instance symbol spotting task is formulated as a subgraph detection problem and solved by predicting the adjacency matrix; 2) a relative spatial encoding (RSE) module explicitly encodes the relative positional and geometric relation among vertices to enhance the vertex attention; 3) a cascaded edge encoding (CEE) module extracts vertex attentions from multiple stages of GAT and treats them as edge encoding to predict the adjacency matrix. The proposed GAT-CADNet is intuitive yet effective and manages to solve the panoptic symbol spotting problem in one consolidated network. Extensive experiments and ablation studies on the public benchmark show that our graph-based approach surpasses existing state-of-the-art methods by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_GAT-CADNet_Graph_Attention_Network_for_Panoptic_Symbol_Spotting_in_CAD_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_GAT-CADNet_Graph_Attention_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_GAT-CADNet_Graph_Attention_Network_for_Panoptic_Symbol_Spotting_in_CAD_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_GAT-CADNet_Graph_Attention_Network_for_Panoptic_Symbol_Spotting_in_CAD_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Granularity Alignment Domain Adaptation for Object Detection | Wenzhang Zhou, Dawei Du, Libo Zhang, Tiejian Luo, Yanjun Wu | Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain. In this paper, we propose a unified multi-granularity alignment based object detection framework towards domain-invariant feature learning. To this end, we encode the dependencies across different granularity perspectives including pixel-, instance-, and category-levels simultaneously to align two domains. Based on pixel-level feature maps from the backbone network, we first develop the omni-scale gated fusion module to aggregate discriminative representations of instances by scale-aware convolutions, leading to robust multi-scale object detection. Meanwhile, the multi-granularity discriminators are proposed to identify which domain different granularities of samples (i.e., pixels, instances, and categories) come from. Notably, we leverage not only the instance discriminability in different categories but also the category consistency between two domains. Extensive experiments are carried out on multiple domain adaptation scenarios, demonstrating the effectiveness of our framework over state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster R-CNN detectors with different backbones. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Multi-Granularity_Alignment_Domain_Adaptation_for_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Multi-Granularity_Alignment_Domain_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16897 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Multi-Granularity_Alignment_Domain_Adaptation_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Multi-Granularity_Alignment_Domain_Adaptation_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
LARGE: Latent-Based Regression Through GAN Semantics | Yotam Nitzan, Rinon Gal, Ofir Brenner, Daniel Cohen-Or | We propose a novel method for solving regression tasks using few-shot or weak supervision. At the core of our method is the fundamental observation that GANs are incredibly successful at encoding semantic information within their latent space, even in a completely unsupervised setting. For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner. These directions have been widely used in GAN-based image editing. In this work, we leverage them for few-shot regression. Specifically, we make the simple observation that distances traversed along such directions are good features for downstream tasks - reliably gauging the magnitude of a property in an image. In the absence of explicit supervision, we use these distances to solve tasks such as sorting a collection of images, and ordinal regression. With a few labels -- as little as two -- we calibrate these distances to real-world values and convert a pre-trained GAN into a state-of-the-art few-shot regression model. This enables solving regression tasks on datasets and attributes which are difficult to produce quality supervision for. Extensive experimental evaluations demonstrate that our method can be applied across a wide range of domains, leverage multiple latent direction discovery frameworks, and achieve state-of-the-art results in few-shot and low-supervision settings, even when compared to methods designed to tackle a single task. Code is available on our project website. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nitzan_LARGE_Latent-Based_Regression_Through_GAN_Semantics_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nitzan_LARGE_Latent-Based_Regression_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.11186 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nitzan_LARGE_Latent-Based_Regression_Through_GAN_Semantics_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nitzan_LARGE_Latent-Based_Regression_Through_GAN_Semantics_CVPR_2022_paper.html | CVPR 2022 | null |
Are Multimodal Transformers Robust to Missing Modality? | Mengmeng Ma, Jian Ren, Long Zhao, Davide Testuggine, Xi Peng | Multimodal data collected from the real world are often imperfect due to missing modalities. Therefore multimodal models that are robust against modal-incomplete data are highly preferred. Recently, Transformer models have shown great success in processing multimodal data. However, existing work has been limited to either architecture designs or pre-training strategies; whether Transformer models are naturally robust against missing-modal data has rarely been investigated. In this paper, we present the first-of-its-kind work to comprehensively investigate the behavior of Transformers in the presence of modal-incomplete data. Unsurprising, we find Transformer models are sensitive to missing modalities while different modal fusion strategies will significantly affect the robustness. What surprised us is that the optimal fusion strategy is dataset dependent even for the same Transformer model; there does not exist a universal strategy that works in general cases. Based on these findings, we propose a principle method to improve the robustness of Transformer models by automatically searching for an optimal fusion strategy regarding input data. Experimental validations on three benchmarks support the superior performance of the proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Are_Multimodal_Transformers_Robust_to_Missing_Modality_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.05454 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Are_Multimodal_Transformers_Robust_to_Missing_Modality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Are_Multimodal_Transformers_Robust_to_Missing_Modality_CVPR_2022_paper.html | CVPR 2022 | null |
Degradation-Agnostic Correspondence From Resolution-Asymmetric Stereo | Xihao Chen, Zhiwei Xiong, Zhen Cheng, Jiayong Peng, Yueyi Zhang, Zheng-Jun Zha | In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty of obtaining ground-truth disparity labels in diverse real-world systems, we start from an unsupervised learning perspective. However, resolution asymmetry caused by unknown degradations between two views hinders the effectiveness of the generally assumed photometric consistency. To overcome this challenge, we propose to impose the consistency between two views in a feature space instead of the image space, named feature-metric consistency. Interestingly, we find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features. These features can then be utilized to formulate a feature-metric loss to avoid the photometric inconsistency. Moreover, we introduce a self-boosting strategy to optimize the feature extractor progressively, which further strengthens the feature-metric consistency. Experiments on both simulated datasets with various degradations and a self-collected real-world dataset validate the superior performance of the proposed method over existing solutions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Degradation-Agnostic_Correspondence_From_Resolution-Asymmetric_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Degradation-Agnostic_Correspondence_From_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01429 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Degradation-Agnostic_Correspondence_From_Resolution-Asymmetric_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Degradation-Agnostic_Correspondence_From_Resolution-Asymmetric_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
Fisher Information Guidance for Learned Time-of-Flight Imaging | Jiaqu Li, Tao Yue, Sijie Zhao, Xuemei Hu | Indirect Time-of-Flight (ToF) imaging is widely applied in practice for its superiorities on cost and spatial resolution. However, lower signal-to-noise ratio (SNR) of measurement leads to larger error in ToF imaging, especially for imaging scenes with strong ambient light or long distance. In this paper, we propose a Fisher-information guided framework to jointly optimize the coding functions (light modulation and sensor demodulation functions) and the reconstruction network of iToF imaging, with the supervision of the proposed discriminative fisher loss. By introducing the differentiable modeling of physical imaging process considering various real factors and constraints, e.g., light-falloff with distance, physical implementability of coding functions, etc., followed by a dual-branch depth reconstruction neural network, the proposed method could learn the optimal iToF imaging system in an end-to-end manner. The effectiveness of the proposed method is extensively verified with both simulations and prototype experiments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Fisher_Information_Guidance_for_Learned_Time-of-Flight_Imaging_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Fisher_Information_Guidance_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Fisher_Information_Guidance_for_Learned_Time-of-Flight_Imaging_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Fisher_Information_Guidance_for_Learned_Time-of-Flight_Imaging_CVPR_2022_paper.html | CVPR 2022 | null |
VRDFormer: End-to-End Video Visual Relation Detection With Transformers | Sipeng Zheng, Shizhe Chen, Qin Jin | Visual relation understanding plays an essential role for holistic video understanding. Most previous works adopt a multi-stage framework for video visual relation detection (VidVRD), which cannot capture long-term spatiotemporal contexts in different stages and also suffers from inefficiency. In this paper, we propose a transformerbased framework called VRDFormer to unify these decoupling stages. Our model exploits a query-based approach to autoregressively generate relation instances. We specifically design static queries and recurrent queries to enable efficient object pair tracking with spatio-temporal contexts. The model is jointly trained with object pair detection and relation classification. Extensive experiments on two benchmark datasets, ImageNet-VidVRD and VidOR, demonstrate the effectiveness of the proposed VRDFormer, which achieves the state-of-the-art performance on both relation detection and relation tagging tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_VRDFormer_End-to-End_Video_Visual_Relation_Detection_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_VRDFormer_End-to-End_Video_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_VRDFormer_End-to-End_Video_Visual_Relation_Detection_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_VRDFormer_End-to-End_Video_Visual_Relation_Detection_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Robust Federated Learning With Noisy and Heterogeneous Clients | Xiuwen Fang, Mang Ye | Model heterogeneous federated learning is a challenging task since each client independently designs its own model. Due to the annotation difficulty and free-riding participant issue, the local client usually contains unavoidable and varying noises, which cannot be effectively addressed by existing algorithms. This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients. We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the models feedback by utilizing public data, which does not require additional shared global models for collaboration. (2) For internal label noise, we apply a robust noise-tolerant loss function to reduce the negative effects. (3) For challenging noisy feedback from other participants, we design a novel client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in reducing the negative effects of different noise rates/types under both model homogeneous and heterogeneous federated learning settings, consistently outperforming existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_Robust_Federated_Learning_With_Noisy_and_Heterogeneous_Clients_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fang_Robust_Federated_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Robust_Federated_Learning_With_Noisy_and_Heterogeneous_Clients_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Robust_Federated_Learning_With_Noisy_and_Heterogeneous_Clients_CVPR_2022_paper.html | CVPR 2022 | null |
Enabling Equivariance for Arbitrary Lie Groups | Lachlan E. MacDonald, Sameera Ramasinghe, Simon Lucey | Although provably robust to translational perturbations, convolutional neural networks (CNNs) are known to suffer from extreme performance degradation when presented at test time with more general geometric transformations of inputs. Recently, this limitation has motivated a shift in focus from CNNs to Capsule Networks (CapsNets). However, CapsNets suffer from admitting relatively few theoretical guarantees of invariance. We introduce a rigourous mathematical framework to permit invariance to any Lie group of warps, exclusively using convolutions (over Lie groups), without the need for capsules. Previous work on group convolutions has been hampered by strong assumptions about the group, which precludes the application of such techniques to common warps in computer vision such as affine and homographic. Our framework enables the implementation of group convolutions over any finite-dimensional Lie group. We empirically validate our approach on the benchmark affine-invariant classification task, where we achieve 30% improvement in accuracy against conventional CNNs while outperforming most CapsNets. As further illustration of the generality of our framework, we train a homography-convolutional model which achieves superior robustness on a homography-perturbed dataset, where CapsNet results degrade. | https://openaccess.thecvf.com/content/CVPR2022/papers/MacDonald_Enabling_Equivariance_for_Arbitrary_Lie_Groups_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/MacDonald_Enabling_Equivariance_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.08251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/MacDonald_Enabling_Equivariance_for_Arbitrary_Lie_Groups_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/MacDonald_Enabling_Equivariance_for_Arbitrary_Lie_Groups_CVPR_2022_paper.html | CVPR 2022 | null |
Unbiased Teacher v2: Semi-Supervised Object Detection for Anchor-Free and Anchor-Based Detectors | Yen-Cheng Liu, Chih-Yao Ma, Zsolt Kira | With the recent development of Semi-Supervised Object Detection (SS-OD) techniques, object detectors can be improved by using a limited amount of labeled data and abundant unlabeled data. However, there are still two challenges that are not addressed: (1) there is no prior SS-OD work on anchor-free detectors, and (2) prior works are ineffective when pseudo-labeling bounding box regression. In this paper, we present Unbiased Teacher v2, which shows the generalization of SS-OD method to anchor-free detectors and also introduces Listen2Student mechanism for the unsupervised regression loss. Specifically, we first present a study examining the effectiveness of existing SS-OD methods on anchor-free detectors and find that they achieve much lower performance improvements under the semi-supervised setting. We also observe that box selection with centerness and the localization-based labeling used in anchor-free detectors cannot work well under the semi-supervised setting. On the other hand, our Listen2Student mechanism explicitly prevents misleading pseudo-labels in the training of bounding box regression; we specifically develop a novel pseudo-labeling selection mechanism based on the Teacher and Student's relative uncertainties. This idea contributes to favorable improvement in the regression branch in the semi-supervised setting. Our method, which works for both anchor-free and anchor-based methods, consistently performs favorably against the state-of-the-art methods in VOC, COCO-standard, and COCO-additional. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Unbiased_Teacher_v2_Semi-Supervised_Object_Detection_for_Anchor-Free_and_Anchor-Based_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Unbiased_Teacher_v2_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Unbiased_Teacher_v2_Semi-Supervised_Object_Detection_for_Anchor-Free_and_Anchor-Based_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Unbiased_Teacher_v2_Semi-Supervised_Object_Detection_for_Anchor-Free_and_Anchor-Based_CVPR_2022_paper.html | CVPR 2022 | null |
GPU-Based Homotopy Continuation for Minimal Problems in Computer Vision | Chiang-Heng Chien, Hongyi Fan, Ahmad Abdelfattah, Elias Tsigaridas, Stanimire Tomov, Benjamin Kimia | Systems of polynomial equations arise frequently in computer vision, especially in multiview geometry problems. Traditional methods for solving these systems typically aim to eliminate variables to reach a univariate polynomial, e.g., a tenth-order polynomial for 5-point pose estimation, using clever manipulations, or more generally using Grobner basis, resultants, and elimination templates, leading to successful algorithms for multiview geometry and other problems. However, these methods do not work when the problem is complex and when they do, they face efficiency and stability issues. Homotopy Continuation (HC) can solve more complex problems without the stability issues, and with guarantees of a global solution, but they are known to be slow. In this paper we show that HC can be parallelized on a GPU, showing significant speedups up to 56 times on polynomial benchmarks. We also show that GPU-HC can be generically applied to a range of computer vision problems, including 4-view triangulation and trifocal pose estimation with unknown focal length, which cannot be solved with elimination template but they can be efficiently solved with HC. GPU-HC opens the door to easy formulation and solution of a range of computer vision problems. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chien_GPU-Based_Homotopy_Continuation_for_Minimal_Problems_in_Computer_Vision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chien_GPU-Based_Homotopy_Continuation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03444 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chien_GPU-Based_Homotopy_Continuation_for_Minimal_Problems_in_Computer_Vision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chien_GPU-Based_Homotopy_Continuation_for_Minimal_Problems_in_Computer_Vision_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Pixel-Level Distinctions for Video Highlight Detection | Fanyue Wei, Biao Wang, Tiezheng Ge, Yuning Jiang, Wen Li, Lixin Duan | The goal of video highlight detection is to select the most attractive segments from a long video to depict the most interesting parts of the video. Existing methods typically focus on modeling relationship between different video segments in order to learning a model that can assign highlight scores to these segments; however, these approaches do not explicitly consider the contextual dependency within individual segments. To this end, we propose to learn pixel-level distinctions to improve the video highlight detection. This pixel-level distinction indicates whether or not each pixel in one video belongs to an interesting section. The advantages of modeling such fine-level distinctions are two-fold. First, it allows us to exploit the temporal and spatial relations of the content in one video, since the distinction of a pixel in one frame is highly dependent on both the content before this frame and the content around this pixel in this frame. Second, learning the pixel-level distinction also gives a good explanation to the video highlight task regarding what contents in a highlight segment will be attractive to people. We design an encoder-decoder network to estimate the pixel-level distinction, in which we leverage the 3D convolutional neural networks to exploit the temporal context information, and further take advantage of the visual saliency to model the spatial distinction. State-of-the-art performance on three public benchmarks clearly validates the effectiveness of our framework for video highlight detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_Learning_Pixel-Level_Distinctions_for_Video_Highlight_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_Learning_Pixel-Level_Distinctions_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Learning_Pixel-Level_Distinctions_for_Video_Highlight_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_Learning_Pixel-Level_Distinctions_for_Video_Highlight_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Noise Distribution Adaptive Self-Supervised Image Denoising Using Tweedie Distribution and Score Matching | Kwanyoung Kim, Taesung Kwon, Jong Chul Ye | Tweedie distributions are a special case of exponential dispersion models, which are often used in classical statistics as distributions for generalized linear models. Here, we reveal that Tweedie distributions also play key roles in modern deep learning era, leading to a distribution independent self-supervised image denoising formula without clean reference images. Specifically, by combining with the recent Noise2Score self-supervised image denoising approach and the saddle point approximation of Tweedie distribution, we can provide a general closed-form denoising formula that can be used for large classes of noise distributions without ever knowing the underlying noise distribution. Similar to the original Noise2Score, the new approach is composed of two successive steps: score matching using perturbed noisy images, followed by a closed form image denoising formula via distribution-independent Tweedie's formula. This also suggests a systematic algorithm to estimate the noise model and noise parameters for a given noisy image data set. Through extensive experiments, we demonstrate that the proposed method can accurately estimate noise models and parameters, and provide the state-of-the-art self-supervised image denoising performance in the benchmark dataset and real-world dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Noise_Distribution_Adaptive_Self-Supervised_Image_Denoising_Using_Tweedie_Distribution_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Noise_Distribution_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Noise_Distribution_Adaptive_Self-Supervised_Image_Denoising_Using_Tweedie_Distribution_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Noise_Distribution_Adaptive_Self-Supervised_Image_Denoising_Using_Tweedie_Distribution_and_CVPR_2022_paper.html | CVPR 2022 | null |
Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation | Yihan Wang, Muyang Li, Han Cai, Wei-Ming Chen, Song Han | Pose estimation plays a critical role in human-centered vision applications. However, it is difficult to deploy state-of-the-art HRNet-based pose estimation models on resource-constrained edge devices due to the high computational cost (more than 150 GMACs per frame). In this paper, we study efficient architecture design for real-time multi-person pose estimation on edge. We reveal that HRNet's high-resolution branches are redundant for models at the low-computation region via our gradual shrinking experiments. Removing them improves both efficiency and performance. Inspired by this finding, we design LitePose, an efficient single-branch architecture for pose estimation, and introduce two simple approaches to enhance the capacity of LitePose, including fusion deconv head and large kernel conv. On mobile platforms, LitePose reduces the latency by up to 5.0x without sacrificing performance, compared with prior state-of-the-art efficient pose estimation models, pushing the frontier of real-time multi-person pose estimation on edge. Our code and pre-trained models are released at https://github.com/mit-han-lab/litepose. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Lite_Pose_Efficient_Architecture_Design_for_2D_Human_Pose_Estimation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.01271 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Lite_Pose_Efficient_Architecture_Design_for_2D_Human_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Lite_Pose_Efficient_Architecture_Design_for_2D_Human_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Boosting Black-Box Attack With Partially Transferred Conditional Adversarial Distribution | Yan Feng, Baoyuan Wu, Yanbo Fan, Li Liu, Zhifeng Li, Shu-Tao Xia | This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while other information such as model parameters or the training datasets are unknown. One promising approach to improve attack performance is utilizing the adversarial transferability between some white-box surrogate models and the target model (i.e., the attacked model). However, due to the possible differences on model architectures and training datasets between surrogate and target models, dubbed "surrogate biases", the contribution of adversarial transferability to improving the attack performance may be weakened. To tackle this issue, we innovatively propose a black-box attack method by developing a novel mechanism of adversarial transferability, which is robust to the surrogate biases. The general idea is transferring partial parameters of the conditional adversarial distribution (CAD) of surrogate models, while learning the untransferred parameters based on queries to the target model, to keep the flexibility to adjust the CAD of the target model on any new benign sample. Extensive experiments on benchmark datasets and attacking against real-world API demonstrate the superior attack performance of the proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Feng_Boosting_Black-Box_Attack_With_Partially_Transferred_Conditional_Adversarial_Distribution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Feng_Boosting_Black-Box_Attack_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2006.08538 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Boosting_Black-Box_Attack_With_Partially_Transferred_Conditional_Adversarial_Distribution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Feng_Boosting_Black-Box_Attack_With_Partially_Transferred_Conditional_Adversarial_Distribution_CVPR_2022_paper.html | CVPR 2022 | null |
CLIPstyler: Image Style Transfer With a Single Text Condition | Gihyun Kwon, Jong Chul Ye | Existing neural style transfer methods require reference style images to transfer texture information of style images to content images. However, in many practical situations, users may not have reference style images but still be interested in transferring styles by just imagining them. In order to deal with such applications, we propose a new framework that enables a style transfer 'without' a style image, but only with a text description of the desired style. Using the pre-trained text-image embedding model of CLIP, we demonstrate the modulation of the style of content images only with a single text condition. Specifically, we propose a patch-wise text-image matching loss with multiview augmentations for realistic texture transfer. Extensive experimental results confirmed the successful image style transfer with realistic textures that reflect semantic query texts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kwon_CLIPstyler_Image_Style_Transfer_With_a_Single_Text_Condition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kwon_CLIPstyler_Image_Style_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00374 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kwon_CLIPstyler_Image_Style_Transfer_With_a_Single_Text_Condition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kwon_CLIPstyler_Image_Style_Transfer_With_a_Single_Text_Condition_CVPR_2022_paper.html | CVPR 2022 | null |
Ray Priors Through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation | Jian Zhang, Yuanqing Zhang, Huan Fu, Xiaowei Zhou, Bowen Cai, Jinchi Huang, Rongfei Jia, Binqiang Zhao, Xing Tang | Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images. A main limitation of conventional NeRFs is that they often fail to produce high-quality renderings under novel viewpoints that are significantly different from the training viewpoints. In this paper, instead of exploiting few-shot image synthesis, we study the novel view extrapolation setting that (1) the training images can well describe an object, and (2) there is a notable discrepancy between the training and test viewpoints' distributions. We present RapNeRF (RAy Priors) as a solution. Our insight is that the inherent appearances of a 3D surface's arbitrary visible projections should be consistent. We thus propose a random ray casting policy that allows training unseen views using seen views. Furthermore, we show that a ray atlas pre-computed from the observed rays' viewing directions could further enhance the rendering quality for extrapolated views. A main limitation is that RapNeRF would remove the strong view-dependent effects because it leverages the multi-view consistency property. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Ray_Priors_Through_Reprojection_Improving_Neural_Radiance_Fields_for_Novel_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.05922 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Ray_Priors_Through_Reprojection_Improving_Neural_Radiance_Fields_for_Novel_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Ray_Priors_Through_Reprojection_Improving_Neural_Radiance_Fields_for_Novel_CVPR_2022_paper.html | CVPR 2022 | null |
Spatio-Temporal Relation Modeling for Few-Shot Action Recognition | Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Bernard Ghanem | We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discriminability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Local patch-level enrichment captures the appearance-based characteristics of actions. On the other hand, global frame-level enrichment explicitly encodes the broad temporal context, thereby capturing the relevant object features over time. The resulting spatio-temporally enriched representations are then utilized to learn the relational matching between query and support action sub-sequences. We further introduce a query-class similarity classifier on the patch-level enriched features to enhance class-specific feature discriminability by reinforcing the feature learning at different stages in the proposed framework. Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101. Our extensive ablation study reveals the benefits of the proposed contributions. Furthermore, our approach sets a new state-of-the-art on all four benchmarks. On the challenging SSv2 benchmark, our approach achieves an absolute gain of 3.5% in classification accuracy, as compared to the best existing method in the literature. Our code and models are available at https://github.com/Anirudh257/strm. | https://openaccess.thecvf.com/content/CVPR2022/papers/Thatipelli_Spatio-Temporal_Relation_Modeling_for_Few-Shot_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Thatipelli_Spatio-Temporal_Relation_Modeling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05132 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Thatipelli_Spatio-Temporal_Relation_Modeling_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Thatipelli_Spatio-Temporal_Relation_Modeling_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian | Jihyun Lee, Minhyuk Sung, Hyunjin Kim, Tae-Kyun Kim | We propose a framework that can deform an object in a 2D image as it exists in 3D space. Most existing methods for 3D-aware image manipulation are limited to (1) only changing the global scene information or depth, or (2) manipulating an object of specific categories. In this paper, we present a 3D-aware image deformation method with minimal restrictions on shape category and deformation type. While our framework leverages 2D-to-3D reconstruction, we argue that reconstruction is not sufficient for realistic deformations due to the vulnerability to topological errors. Thus, we propose to take a supervised learning-based approach to predict the shape Laplacian of the underlying volume of a 3D reconstruction represented as a point cloud. Given the deformation energy calculated using the predicted shape Laplacian and user-defined deformation handles (e.g., keypoints), we obtain bounded biharmonic weights to model plausible handle-based image deformation. In the experiments, we present our results of deforming 2D character and clothed human images. We also quanti- tatively show that our approach can produce more accurate deformation weights compared to alternative methods (i.e., mesh reconstruction and point cloud Laplacian methods). | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Pop-Out_Motion_3D-Aware_Image_Deformation_via_Learning_the_Shape_Laplacian_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Pop-Out_Motion_3D-Aware_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15235 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Pop-Out_Motion_3D-Aware_Image_Deformation_via_Learning_the_Shape_Laplacian_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Pop-Out_Motion_3D-Aware_Image_Deformation_via_Learning_the_Shape_Laplacian_CVPR_2022_paper.html | CVPR 2022 | null |
Volumetric Bundle Adjustment for Online Photorealistic Scene Capture | Ronald Clark | Efficient photorealistic scene capture is a challenging task. Current online reconstruction systems can operate very efficiently, but images generated from the models captured by these systems are often not photorealistic. Recent approaches based on neural volume rendering can render novel views at high fidelity, but they often require a long time to train, making them impractical for applications that require real-time scene capture. In this paper, we propose a system that can reconstruct photorealistic models of complex scenes in an efficient manner. Our system processes images online, i.e. it can obtain a good quality estimate of both the scene geometry and appearance at roughly the same rate the video is captured. To achieve the efficiency, we propose a hierarchical feature volume using VDB grids. This representation is memory efficient and allows for fast querying of the scene information. Secondly, we introduce a novel optimization technique that improves the efficiency of the bundle adjustment which allows our system to converge to the target camera poses and scene geometry much faster. Experiments on real-world scenes show that our method outperforms existing systems in terms of efficiency and capture quality. To the best of our knowledge, this is the first method that can achieve online photorealistic scene capture. | https://openaccess.thecvf.com/content/CVPR2022/papers/Clark_Volumetric_Bundle_Adjustment_for_Online_Photorealistic_Scene_Capture_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Clark_Volumetric_Bundle_Adjustment_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Clark_Volumetric_Bundle_Adjustment_for_Online_Photorealistic_Scene_Capture_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Clark_Volumetric_Bundle_Adjustment_for_Online_Photorealistic_Scene_Capture_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Person Extreme Motion Prediction | Wen Guo, Xiaoyu Bie, Xavier Alameda-Pineda, Francesc Moreno-Noguer | Human motion prediction aims to forecast future poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackled for single humans in isolation. In this paper, we explore this problem when dealing with humans performing collaborative tasks, we seek to predict the future motion of two interacted persons given two sequences of their past skeletons. We propose a novel cross interaction attention mechanism that exploits historical information of both persons, and learns to predict cross dependencies between the two pose sequences. Since no dataset to train such interactive situations is available, we collected ExPI (Extreme Pose Interaction) dataset, a new lab-based person interaction dataset of professional dancers performing Lindy-hop dancing actions, which contains 115 sequences with 30K frames annotated with 3D body poses and shapes. We thoroughly evaluate our cross interaction network on ExPI and show that both in short- and long-term predictions, it consistently outperforms state-of-the-art methods for single-person motion prediction. Our code and dataset are available at: https://team.inria.fr/robotlearn/multi-person-extreme-motion-prediction/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_Multi-Person_Extreme_Motion_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network | Byung-Kwan Lee, Junho Kim, Yong Man Ro | Adversarial examples provoke weak reliability and potential security issues in deep neural networks. Although adversarial training has been widely studied to improve adversarial robustness, it works in an over-parameterized regime and requires high computations and large memory budgets. To bridge adversarial robustness and model compression, we propose a novel adversarial pruning method, Masking Adversarial Damage (MAD) that employs second-order information of adversarial loss. By using it, we can accurately estimate adversarial saliency for model parameters and determine which parameters can be pruned without weakening adversarial robustness. Furthermore, we reveal that model parameters of initial layer are highly sensitive to the adversarial examples and show that compressed feature representation retains semantic information for the target objects. Through extensive experiments on three public datasets, we demonstrate that MAD effectively prunes adversarially trained networks without loosing adversarial robustness and shows better performance than previous adversarial pruning methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Masking_Adversarial_Damage_Finding_Adversarial_Saliency_for_Robust_and_Sparse_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_Masking_Adversarial_Damage_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02738 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Masking_Adversarial_Damage_Finding_Adversarial_Saliency_for_Robust_and_Sparse_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Masking_Adversarial_Damage_Finding_Adversarial_Saliency_for_Robust_and_Sparse_CVPR_2022_paper.html | CVPR 2022 | null |
Channel Balancing for Accurate Quantization of Winograd Convolutions | Vladimir Chikin, Vladimir Kryzhanovskiy | It is well known that Winograd convolution algorithms speed up the widely used small-size convolutions. However, the problem of quantization of Winograd convolutions is challenging - while quantization of slower Winograd algorithms does not cause problems, quantization of faster Winograd algorithms often leads to a significant drop in the quality of models. We introduce a novel class of Winograd algorithms that balances the filter and input channels in the Winograd domain. Unlike traditional Winograd convolutions, the proposed convolution balances the ranges of input channels on the forward pass by scaling the input tensor using special balancing coefficients (the filter channels are balanced offline). As a result of balancing, the inputs and filters of the Winograd convolution are much easier to quantize. Thus, the proposed technique allows us to obtain models with quantized Winograd convolutions, the quality of which is significantly higher than the quality of models with traditional quantized Winograd convolutions. Moreover, we propose a special direct algorithm for calculating the balancing coefficients, which does not require additional model training. This algorithm makes it easy to obtain the post-training quantized balanced Winograd convolutions - one should just feed a few data samples to the model without training to calibrate special parameters. In addition, it is possible to initialize the balancing coefficients using this algorithm and further train them as trainable variables during Winograd quantization-aware training for greater quality improvement. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chikin_Channel_Balancing_for_Accurate_Quantization_of_Winograd_Convolutions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chikin_Channel_Balancing_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chikin_Channel_Balancing_for_Accurate_Quantization_of_Winograd_Convolutions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chikin_Channel_Balancing_for_Accurate_Quantization_of_Winograd_Convolutions_CVPR_2022_paper.html | CVPR 2022 | null |
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis From Sparse Inputs | Michael Niemeyer, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan | Neural Radiance Fields (NeRF) have emerged as a powerful representation for the task of novel view synthesis due to their simplicity and state-of-the-art performance. Though NeRF can produce photorealistic renderings of unseen viewpoints when many input views are available, its performance drops significantly when this number is reduced. We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training. We address this by regularizing the geometry and appearance of patches rendered from unobserved viewpoints, and annealing the ray sampling space during training. We additionally use a normalizing flow model to regularize the color of unobserved viewpoints. Our model outperforms not only other methods that optimize over a single scene, but in many cases also conditional models that are extensively pre-trained on large multi-view datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Niemeyer_RegNeRF_Regularizing_Neural_Radiance_Fields_for_View_Synthesis_From_Sparse_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.00724 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Niemeyer_RegNeRF_Regularizing_Neural_Radiance_Fields_for_View_Synthesis_From_Sparse_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Niemeyer_RegNeRF_Regularizing_Neural_Radiance_Fields_for_View_Synthesis_From_Sparse_CVPR_2022_paper.html | CVPR 2022 | null |
Structured Local Radiance Fields for Human Avatar Modeling | Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, Yebin Liu | It is extremely challenging to create an animatable clothed human avatar from RGB videos, especially for loose clothes due to the difficulties in motion modeling. To address this problem, we introduce a novel representation on the basis of recent neural scene rendering techniques. The core of our representation is a set of structured local radiance fields, which are anchored to the pre-defined nodes sampled on a statistical human body template. These local radiance fields not only leverage the flexibility of implicit representation in shape and appearance modeling, but also factorize cloth deformations into skeleton motions, node residual translations and the dynamic detail variations inside each individual radiance field. To learn our representation from RGB data and facilitate pose generalization, we propose to learn the node translations and the detail variations in a conditional generative latent space. Overall, our method enables automatic construction of animatable human avatars for various types of clothes without the need for scanning subject-specific templates, and can generate realistic images with dynamic details for novel poses. Experiment show that our method outperforms state-of-the-art methods both qualitatively and quantitatively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Structured_Local_Radiance_Fields_for_Human_Avatar_Modeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Structured_Local_Radiance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14478 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Structured_Local_Radiance_Fields_for_Human_Avatar_Modeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Structured_Local_Radiance_Fields_for_Human_Avatar_Modeling_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Noiseless Object Contours for Weakly Supervised Semantic Segmentation | Jing Li, Junsong Fan, Zhaoxiang Zhang | Image-level label based weakly supervised semantic segmentation has attracted much attention since image labels are very easy to obtain. Existing methods usually generate pseudo labels from class activation map (CAM) and then train a segmentation model. CAM usually highlights partial objects and produce incomplete pseudo labels. Some methods explore object contour by training a contour model with CAM seed label supervision and then propagate CAM score from discriminative regions to non-discriminative regions with contour guidance. The propagation process suffers from the noisy intra-object contours, and inadequate propagation results produce incomplete pseudo labels. This is because the coarse CAM seed label lacks sufficient precise semantic information to suppress contour noise. In this paper, we train a SANCE model which utilizes an auxiliary segmentation module to supplement high-level semantic information for contour training by backbone feature sharing and online label supervision. The auxiliary segmentation module also provides more accurate localization map than CAM for pseudo label generation. We evaluate our approach on Pascal VOC 2012 and MS COCO 2014 benchmarks and achieve state-of-the-art performance, demonstrating the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Towards_Noiseless_Object_Contours_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Towards_Noiseless_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_Noiseless_Object_Contours_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_Noiseless_Object_Contours_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.