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Learning To Memorize Feature Hallucination for One-Shot Image Generation | Yu Xie, Yanwei Fu, Ying Tai, Yun Cao, Junwei Zhu, Chengjie Wang | This paper studies the task of One-Shot image Generation (OSG), where generation network learned on base dataset should be generalizable to synthesize images of novel categories with only one available sample per novel category. Most existing methods for feature transfer in one-shot image generation only learn reusable features implicitly on pre-training tasks. Such methods would be likely to overfit pre-training tasks. In this paper, we propose a novel model to explicitly learn and memorize reusable features that can help hallucinate novel category images. To be specific, our algorithm learns to decompose image features into the Category-Related (CR) and Category-Independent (CI) features. Our model learning to memorize class-independent CI features which are further utilized by our feature hallucination component to generate target novel category images. We validate our model on several benchmarks. Extensive experiments demonstrate that our model effectively boosts the OSG performance and can generate compelling and diverse samples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Learning_To_Memorize_Feature_Hallucination_for_One-Shot_Image_Generation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Learning_To_Memorize_Feature_Hallucination_for_One-Shot_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Learning_To_Memorize_Feature_Hallucination_for_One-Shot_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis | Zhiqin Chen, Kangxue Yin, Sanja Fidler | In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details. We argue that the traditional way of representing textures with images and linking them to a 3D mesh via UV mapping is more desirable, since synthesizing 2D images is a well-studied problem. We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space, by mapping the corresponding semantic parts of different 3D shapes to the same location in the UV space. As a result, textures are aligned across objects, and can thus be easily synthesized by generative models of images. Texture alignment is learned in an unsupervised manner by a simple yet effective texture alignment module, taking inspiration from traditional works on linear subspace learning. The learned UV mapping and aligned texture representations enable a variety of applications including texture transfer, texture synthesis, and textured single view 3D reconstruction. We conduct experiments on multiple datasets to demonstrate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_AUV-Net_Learning_Aligned_UV_Maps_for_Texture_Transfer_and_Synthesis_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_AUV-Net_Learning_Aligned_UV_Maps_for_Texture_Transfer_and_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_AUV-Net_Learning_Aligned_UV_Maps_for_Texture_Transfer_and_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Open-Vocabulary One-Stage Detection With Hierarchical Visual-Language Knowledge Distillation | Zongyang Ma, Guan Luo, Jin Gao, Liang Li, Yuxin Chen, Shaoru Wang, Congxuan Zhang, Weiming Hu | Open-vocabulary object detection aims to detect novel object categories beyond the training set. The advanced open-vocabulary two-stage detectors employ instance-level visual-to-visual knowledge distillation to align the visual space of the detector with the semantic space of the Pre-trained Visual-Language Model (PVLM). However, in the more efficient one-stage detector, the absence of class-agnostic object proposals hinders the knowledge distillation on unseen objects, leading to severe performance degradation. In this paper, we propose a hierarchical visual-language knowledge distillation method, i.e., HierKD, for open-vocabulary one-stage detection. Specifically, a global-level knowledge distillation is explored to transfer the knowledge of unseen categories from the PVLM to the detector. Moreover, we combine the proposed global-level knowledge distillation and the common instance-level knowledge distillation in a hierarchical structure to learn the knowledge of seen and unseen categories simultaneously. Extensive experiments on MS-COCO show that our method significantly surpasses the previous best one-stage detector with 11.9% and 6.7% AP50 gains under the zero-shot detection and generalized zero-shot detection settings, and reduces the AP50 performance gap from 14% to 7.3% compared to the best two-stage detector. Code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Open-Vocabulary_One-Stage_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10593 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Open-Vocabulary_One-Stage_Detection_With_Hierarchical_Visual-Language_Knowledge_Distillation_CVPR_2022_paper.html | CVPR 2022 | null |
Glass: Geometric Latent Augmentation for Shape Spaces | Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir G. Kim, Matthew Fisher, Niloy J. Mitra | We investigate the problem of training generative models on very sparse collections of 3D models. Particularly, instead of using difficult-to-obtain large sets of 3D models, we demonstrate that geometrically-motivated energy functions can be used to effectively augment and boost only a sparse collection of example (training) models. Technically, we analyze the Hessian of the as-rigid-as-possible (ARAP) energy to adaptively sample from and project to the underlying (local) shape space, and use the augmented dataset to train a variational autoencoder (VAE). We iterate the process, of building latent spaces of VAE and augmenting the associated dataset, to progressively reveal a richer and more expressive generative space for creating geometrically and semantically valid samples. We evaluate our method against a set of strong baselines, provide ablation studies, and demonstrate application towards establishing shape correspondences. GLASS produces multiple interesting and meaningful shape variations even when starting from as few as 3-10 training shapes. Our code is available at https: //sanjeevmk.github.io/glass_webpage/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Muralikrishnan_Glass_Geometric_Latent_Augmentation_for_Shape_Spaces_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2108.03225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Muralikrishnan_Glass_Geometric_Latent_Augmentation_for_Shape_Spaces_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Muralikrishnan_Glass_Geometric_Latent_Augmentation_for_Shape_Spaces_CVPR_2022_paper.html | CVPR 2022 | null |
COAP: Compositional Articulated Occupancy of People | Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhöfer, Siyu Tang | We present a novel neural implicit representation for articulated human bodies. Compared to explicit template meshes, neural implicit body representations provide an efficient mechanism for modeling interactions with the environment, which is essential for human motion reconstruction and synthesis in 3D scenes. However, existing neural implicit bodies suffer from either poor generalization on highly articulated poses or slow inference time. In this work, we observe that prior knowledge about the human body's shape and kinematic structure can be leveraged to improve generalization and efficiency. We decompose the full-body geometry into local body parts and employ a part-aware encoder-decoder architecture to learn neural articulated occupancy that models complex deformations locally. Our local shape encoder represents the body deformation of not only the corresponding body part but also the neighboring body parts. The decoder incorporates the geometric constraints of local body shape which significantly improves pose generalization. We demonstrate that our model is suitable for resolving self-intersections and collisions with 3D environments. Quantitative and qualitative experiments show that our method largely outperforms existing solutions in terms of both efficiency and accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mihajlovic_COAP_Compositional_Articulated_Occupancy_of_People_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mihajlovic_COAP_Compositional_Articulated_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06184 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mihajlovic_COAP_Compositional_Articulated_Occupancy_of_People_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mihajlovic_COAP_Compositional_Articulated_Occupancy_of_People_CVPR_2022_paper.html | CVPR 2022 | null |
Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation | Hanqing Wang, Wei Liang, Jianbing Shen, Luc Van Gool, Wenguan Wang | Since the rise of vision-language navigation (VLN), great progress has been made in instruction following -- building a follower to navigate environments under the guidance of instructions. However, far less attention has been paid to the inverse task: instruction generation -- learning a speaker to generate grounded descriptions for navigation routes. Existing VLN methods train a speaker independently and typically treat it as a data augmentation tool for strengthening the follower, while ignoring rich cross-task relations. Here we describe an approach that learns the two tasks simultaneously and exploits their intrinsic correlations to boost the training of each: the follower judges whether the speaker-created instruction explains the original navigation route correctly, and vice versa. Without the need of aligned instruction-path pairs, such cycle-consistent learning scheme is complementary to task-specific training objectives defined on labeled data, and can also be applied over unlabeled paths (sampled without paired instructions). Another agent, called creator, is added to generate counterfactual environments. It greatly changes current scenes yet leaves novel items -- which are crucial for the execution of original instructions -- unchanged. Thus more informative training scenes are synthesized and the three agents compose a powerful VLN learning system. Experiments on a standard benchmark show that our approach improves the performance of various follower models and produces accurate navigation instructions. Our code will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16586 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.html | CVPR 2022 | null |
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization | Damien Teney, Ehsan Abbasnejad, Simon Lucey, Anton van den Hengel | Neural networks trained with SGD were recently shown to rely preferentially on linearly-predictive features and can ignore complex, equally-predictive ones. This simplicity bias can explain their lack of robustness out of distribution (OOD). The more complex the task to learn, the more likely it is that statistical artifacts (i.e. selection biases, spurious correlations) are simpler than the mechanisms to learn. We demonstrate that the simplicity bias can be mitigated and OOD generalization improved. We train a set of similar models to fit the data in different ways using a penalty on the alignment of their input gradients. We show theoretically and empirically that this induces the learning of more complex predictive patterns. OOD generalization fundamentally requires information beyond i.i.d. examples, such as multiple training environments, counterfactual examples, or other side information. Our approach shows that we can defer this requirement to an independent model selection stage. We obtain SOTA results in visual recognition on biased data and generalization across visual domains. The method - the first to evade the simplicity bias - highlights the need for a better understanding and control of inductive biases in deep learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Teney_Evading_the_Simplicity_Bias_Training_a_Diverse_Set_of_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Teney_Evading_the_Simplicity_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.05612 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Teney_Evading_the_Simplicity_Bias_Training_a_Diverse_Set_of_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Teney_Evading_the_Simplicity_Bias_Training_a_Diverse_Set_of_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities | Fadime Sener, Dibyadip Chatterjee, Daniel Shelepov, Kun He, Dipika Singhania, Robert Wang, Angela Yao | Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instructions, and the sequences feature rich and natural variations in action ordering, mistakes, and corrections. Assembly101 is the first multi-view action dataset, with simultaneous static (8) and egocentric (4) recordings. Sequences are annotated with more than 100K coarse and 1M fine-grained action segments, and 18M 3D hand poses. We benchmark on three action understanding tasks: recognition, anticipation and temporal segmentation. Additionally, we propose a novel task of detecting mistakes. The unique recording format and rich set of annotations allow us to investigate generalization to new toys, cross-view transfer, long-tailed distributions, and pose vs. appearance. We envision that Assembly101 will serve as a new challenge to investigate various activity understanding problems. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sener_Assembly101_A_Large-Scale_Multi-View_Video_Dataset_for_Understanding_Procedural_Activities_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sener_Assembly101_A_Large-Scale_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14712 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sener_Assembly101_A_Large-Scale_Multi-View_Video_Dataset_for_Understanding_Procedural_Activities_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sener_Assembly101_A_Large-Scale_Multi-View_Video_Dataset_for_Understanding_Procedural_Activities_CVPR_2022_paper.html | CVPR 2022 | null |
Deterministic Point Cloud Registration via Novel Transformation Decomposition | Wen Chen, Haoang Li, Qiang Nie, Yun-Hui Liu | Given a set of putative 3D-3D point correspondences, we aim to remove outliers and estimate rigid transformation with 6 degrees of freedom (DOF). Simultaneously estimating these 6 DOF is time-consuming due to high-dimensional parameter space. To solve this problem, it is common to decompose 6 DOF, i.e. independently compute 3-DOF rotation and 3-DOF translation. However, high non-linearity of 3-DOF rotation still limits the algorithm efficiency, especially when the number of correspondences is large. In contrast, we propose to decompose 6 DOF into (2+1) and (1+2) DOF. Specifically, (2+1) DOF represent 2-DOF rotation axis and 1-DOF displacement along this rotation axis. (1+2) DOF indicate 1-DOF rotation angle and 2-DOF displacement orthogonal to the above rotation axis. To compute these DOF, we design a novel two-stage strategy based on inlier set maximization. By leveraging branch and bound, we first search for (2+1) DOF, and then the remaining (1+2) DOF. Thanks to the proposed transformation decomposition and two-stage search strategy, our method is deterministic and leads to low computational complexity. We extensively compare our method with state-of-the-art approaches. Our method is more accurate and robust than the approaches that provide similar efficiency to ours. Our method is more efficient than the approaches whose accuracy and robustness are comparable to ours. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Deterministic_Point_Cloud_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Deterministic_Point_Cloud_Registration_via_Novel_Transformation_Decomposition_CVPR_2022_paper.html | CVPR 2022 | null |
Motion-Adjustable Neural Implicit Video Representation | Long Mai, Feng Liu | Implicit neural representation (INR) has been successful in representing static images. Contemporary image-based INR, with the use of Fourier-based positional encoding, can be viewed as a mapping from sinusoidal patterns with different frequencies to image content. Inspired by that view, we hypothesize that it is possible to generate temporally varying content with a single image-based INR model by displacing its input sinusoidal patterns over time. By exploiting the relation between the phase information in sinusoidal functions and their displacements, we incorporate into the conventional image-based INR model a phase-varying positional encoding module, and couple it with a phase-shift generation module that determines the phase-shift values at each frame. The model is trained end-to-end on a video to jointly determine the phase-shift values at each time with the mapping from the phase-shifted sinusoidal functions to the corresponding frame, enabling an implicit video representation. Experiments on a wide range of videos suggest that such a model is capable of learning to interpret phase-varying positional embeddings into the corresponding time-varying content. More importantly, we found that the learned phase-shift vectors tend to capture meaningful temporal and motion information from the video. In particular, manipulating the phase-shift vectors induces meaningful changes in the temporal dynamics of the resulting video, enabling non-trivial temporal and motion editing effects such as temporal interpolation, motion magnification, motion smoothing, and video loop detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mai_Motion-Adjustable_Neural_Implicit_Video_Representation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mai_Motion-Adjustable_Neural_Implicit_Video_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mai_Motion-Adjustable_Neural_Implicit_Video_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Prior for Trajectory Estimation | Chaoyang Wang, Xueqian Li, Jhony Kaesemodel Pontes, Simon Lucey | Neural priors are a promising direction to capture low-level vision statistics without relying on handcrafted regularizers. Recent works have successfully shown the use of neural architecture biases to implicitly regularize image denoising, super-resolution, inpainting, synthesis, scene flow, among others. They do not rely on large-scale datasets to capture prior statistics and thus generalize well to out-of-the-distribution data. Inspired by such advances, we investigate neural priors for trajectory representation. Traditionally, trajectories have been represented by a set of handcrafted bases that have limited expressibility. Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data. We demonstrate how our proposed objective is optimized during runtime to estimate trajectories for two important tasks: Non-Rigid Structure from Motion (NRSfM) and lidar scene flow integration for self-driving scenes. Our results are competitive to many state-of-the-art methods for both tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Neural_Prior_for_Trajectory_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Neural_Prior_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Neural_Prior_for_Trajectory_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Neural_Prior_for_Trajectory_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
DPICT: Deep Progressive Image Compression Using Trit-Planes | Jae-Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang-Su Kim | We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS). First, we transform an image into a latent tensor using an analysis network. Then, we represent the latent tensor in ternary digits (trits) and encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance. Moreover, within each trit-plane, we sort the trits according to their rate-distortion priorities and transmit more important information first. Since the compression network is less optimized for the cases of using fewer trit-planes, we develop a postprocessing network for refining reconstructed images at low rates. Experimental results show that DPICT outperforms conventional progressive codecs significantly, while enabling FGS transmission. Codes are available at https://github.com/jaehanlee-mcl/DPICT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_DPICT_Deep_Progressive_Image_Compression_Using_Trit-Planes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_DPICT_Deep_Progressive_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2112.06334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_DPICT_Deep_Progressive_Image_Compression_Using_Trit-Planes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_DPICT_Deep_Progressive_Image_Compression_Using_Trit-Planes_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation | Rui Peng, Rongjie Wang, Zhenyu Wang, Yawen Lai, Ronggang Wang | Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods. Although these two representations have recently demonstrated their excellent performance, they still have apparent shortcomings, e.g., regression methods tend to overfit due to the indirect learning cost volume, and classification methods cannot directly infer the exact depth due to its discrete prediction. In this paper, we propose a novel representation, termed Unification, to unify the advantages of regression and classification. It can directly constrain the cost volume like classification methods, but also realize the sub-pixel depth prediction like regression methods. To excavate the potential of unification, we design a new loss function named Unified Focal Loss, which is more uniform and reasonable to combat the challenge of sample imbalance. Combining these two unburdened modules, we present a coarse-to-fine framework, that we call UniMVSNet. The results of ranking first on both DTU and Tanks and Temples benchmarks verify that our model not only performs the best but also has the best generalization ability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_Rethinking_Depth_Estimation_for_Multi-View_Stereo_A_Unified_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_Rethinking_Depth_Estimation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.01501 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Rethinking_Depth_Estimation_for_Multi-View_Stereo_A_Unified_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_Rethinking_Depth_Estimation_for_Multi-View_Stereo_A_Unified_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
Long-Tailed Recognition via Weight Balancing | Shaden Alshammari, Yu-Xiong Wang, Deva Ramanan, Shu Kong | In the real open world, data tends to follow long-tailed class distributions, motivating the well-studied long-tailed recognition (LTR) problem. Naive training produces models that are biased toward common classes in terms of higher accuracy. The key to addressing LTR is to balance various aspects including data distribution, training losses, and gradients in learning. We explore an orthogonal direction, weight balancing , motivated by the empirical observation that the naively trained classifier has "artificially" larger weights in norm for common classes (because there exists abundant data to train them, unlike the rare classes). We investigate three techniques to balance weights, L2-normalization, weight decay, and MaxNorm. We first point out that L2-normalization "perfectly" balances per-class weights to be unit norm, but such a hard constraint might prevent classes from learning better classifiers. In contrast, weight decay penalizes larger weights more heavily and so learns small balanced weights; the MaxNorm constraint encourages growing small weights within a norm ball but caps all the weights by the radius. Our extensive study shows that both help learn balanced weights and greatly improve the LTR accuracy. Surprisingly, weight decay, although underexplored in LTR, significantly improves over prior work. Therefore, we adopt a two-stage training paradigm and propose a simple approach to LTR: (1) learning features using the cross-entropy loss by tuning weight decay, and (2) learning classifiers using class-balanced loss by tuning weight decay and MaxNorm. Our approach achieves the state-of-the-art accuracy on five standard benchmarks, serving as a future baseline for long-tailed recognition. | https://openaccess.thecvf.com/content/CVPR2022/papers/Alshammari_Long-Tailed_Recognition_via_Weight_Balancing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alshammari_Long-Tailed_Recognition_via_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.14197 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alshammari_Long-Tailed_Recognition_via_Weight_Balancing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alshammari_Long-Tailed_Recognition_via_Weight_Balancing_CVPR_2022_paper.html | CVPR 2022 | null |
Text to Image Generation With Semantic-Spatial Aware GAN | Wentong Liao, Kai Hu, Michael Ying Yang, Bodo Rosenhahn | Text-to-image synthesis (T2I) aims to generate photo-realistic images which are semantically consistent with the text descriptions. Existing methods are usually built upon conditional generative adversarial networks (GANs) and initialize an image from noise with sentence embedding, and then refine the features with fine-grained word embedding iteratively. A close inspection of their generated images reveals a major limitation: even though the generated image holistically matches the description, individual image regions or parts of somethings are often not recognizable or consistent with words in the sentence, e.g. "a white crown". To address this problem, we propose a novel framework Semantic-Spatial Aware GAN for synthesizing images from input text. Concretely, we introduce a simple and effective Semantic-Spatial Aware block, which (1) learns semantic-adaptive transformation conditioned on text to effectively fuse text features and image features, and (2) learns a semantic mask in a weakly-supervised way that depends on the current text-image fusion process in order to guide the transformation spatially. Experiments on the challenging COCO and CUB bird datasets demonstrate the advantage of our method over the recent state-of-the-art approaches, regarding both visual fidelity and alignment with input text description. Code available at https://github.com/wtliao/text2image. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liao_Text_to_Image_Generation_With_Semantic-Spatial_Aware_GAN_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2104.00567 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Text_to_Image_Generation_With_Semantic-Spatial_Aware_GAN_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liao_Text_to_Image_Generation_With_Semantic-Spatial_Aware_GAN_CVPR_2022_paper.html | CVPR 2022 | null |
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization | M. Jehanzeb Mirza, Jakub Micorek, Horst Possegger, Horst Bischof | Domain adaptation is crucial to adapt a learned model to new scenarios, such as domain shifts or changing data distributions. Current approaches usually require a large amount of labeled or unlabeled data from the shifted domain. This can be a hurdle in fields which require continuous dynamic adaptation or suffer from scarcity of data, e.g. autonomous driving in challenging weather conditions. To address this problem of continuous adaptation to distribution shifts, we propose Dynamic Unsupervised Adaptation (DUA). By continuously adapting the statistics of the batch normalization layers we modify the feature representations of the model. We show that by sequentially adapting a model with only a fraction of unlabeled data, a strong performance gain can be achieved. With even less than 1% of unlabeled data from the target domain, DUA already achieves competitive results to strong baselines. In addition, the computational overhead is minimal in contrast to previous approaches. Our approach is simple, yet effective and can be applied to any architecture which uses batch normalization as one of its components. We show the utility of DUA by evaluating it on a variety of domain adaptation datasets and tasks including object recognition, digit recognition and object detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mirza_The_Norm_Must_Go_On_Dynamic_Unsupervised_Domain_Adaptation_by_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mirza_The_Norm_Must_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mirza_The_Norm_Must_Go_On_Dynamic_Unsupervised_Domain_Adaptation_by_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mirza_The_Norm_Must_Go_On_Dynamic_Unsupervised_Domain_Adaptation_by_CVPR_2022_paper.html | CVPR 2022 | null |
ShapeFormer: Transformer-Based Shape Completion via Sparse Representation | Xingguang Yan, Liqiang Lin, Niloy J. Mitra, Dani Lischinski, Daniel Cohen-Or, Hui Huang | We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant distribution can then be sampled to generate likely completions, each of which exhibits plausible shape details, while being faithful to the input. To facilitate the use of transformers for 3D, we introduce a compact 3D representation, vector quantized deep implicit function (VQDIF), that utilizes spatial sparsity to represent a close approximation of a 3D shape by a short sequence of discrete variables. Experiments demonstrate that ShapeFormer outperforms prior art for shape completion from ambiguous partial inputs in terms of both completion quality and diversity. We also show that our approach effectively handles a variety of shape types, incomplete patterns, and real-world scans. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_ShapeFormer_Transformer-Based_Shape_Completion_via_Sparse_Representation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_ShapeFormer_Transformer-Based_Shape_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2201.10326 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_ShapeFormer_Transformer-Based_Shape_Completion_via_Sparse_Representation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_ShapeFormer_Transformer-Based_Shape_Completion_via_Sparse_Representation_CVPR_2022_paper.html | CVPR 2022 | null |
PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures | Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt | In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-distribution (OOD) robustness, prediction consistency, resilience to adversaries, calibrated uncertainty estimates, and the ability to detect anomalous inputs. However, improving performance towards these goals is often a balancing act that today's methods cannot achieve without sacrificing performance on other safety axes. For instance, adversarial training improves adversarial robustness but sharply degrades other classifier performance metrics. Similarly, strong data augmentation and regularization techniques often improve OOD robustness but harm anomaly detection, raising the question of whether a Pareto improvement on all existing safety measures is possible. To meet this challenge, we design a new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals, which outperforms numerous baselines, is near Pareto-optimal, and roundly improves safety measures. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hendrycks_PixMix_Dreamlike_Pictures_Comprehensively_Improve_Safety_Measures_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hendrycks_PixMix_Dreamlike_Pictures_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hendrycks_PixMix_Dreamlike_Pictures_Comprehensively_Improve_Safety_Measures_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hendrycks_PixMix_Dreamlike_Pictures_Comprehensively_Improve_Safety_Measures_CVPR_2022_paper.html | CVPR 2022 | null |
Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation | Wonhui Park, Dongkwon Jin, Chang-Su Kim | Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an object boundary by a linear combination of the M eigencontours. We also incorporate the eigencontours into an instance segmentation framework. Experimental results demonstrate that the proposed eigencontours can represent object boundaries more effectively and more efficiently than existing descriptors in a low-dimensional space. Furthermore, the proposed algorithm yields meaningful performances on instance segmentation datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Eigencontours_Novel_Contour_Descriptors_Based_on_Low-Rank_Approximation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Eigencontours_Novel_Contour_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15259 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Eigencontours_Novel_Contour_Descriptors_Based_on_Low-Rank_Approximation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Eigencontours_Novel_Contour_Descriptors_Based_on_Low-Rank_Approximation_CVPR_2022_paper.html | CVPR 2022 | null |
Generalizable Cross-Modality Medical Image Segmentation via Style Augmentation and Dual Normalization | Ziqi Zhou, Lei Qi, Xin Yang, Dong Ni, Yinghuan Shi | For medical image segmentation, imagine if a model was only trained using MR images in source domain, how about its performance to directly segment CT images in target domain? This setting, namely generalizable cross-modality segmentation, owning its clinical potential, is much more challenging than other related settings, e.g., domain adaptation. To achieve this goal, we in this paper propose a novel dual-normalization model by leveraging the augmented source-similar and source-dissimilar images during our generalizable segmentation. To be specific, given a single source domain, aiming to simulate the possible appearance change in unseen target domains, we first utilize a nonlinear transformation to augment source-similar and source-dissimilar images. Then, to sufficiently exploit these two types of augmentations, our proposed dual-normalization based model employs a shared backbone yet independent batch normalization layer for separate normalization. Afterward, we put forward a style-based selection scheme to automatically choose the appropriate path in the test stage. Extensive experiments on three publicly available datasets, i.e., BraTS, Cross-Modality Cardiac, and Abdominal Multi-Organ datasets, have demonstrated that our method outperforms other state-of-the-art domain generalization methods. Code is available at https://github.com/zzzqzhou/Dual-Normalization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Generalizable_Cross-Modality_Medical_Image_Segmentation_via_Style_Augmentation_and_Dual_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Generalizable_Cross-Modality_Medical_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11177 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Generalizable_Cross-Modality_Medical_Image_Segmentation_via_Style_Augmentation_and_Dual_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Generalizable_Cross-Modality_Medical_Image_Segmentation_via_Style_Augmentation_and_Dual_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Optical Flow With Kernel Patch Attention | Ao Luo, Fan Yang, Xin Li, Shuaicheng Liu | Optical flow is a fundamental method used for quantitative motion estimation on the image plane. In the deep learning era, most works treat it as a task of 'matching of features', learning to pull matched pixels as close as possible in feature space and vice versa. However, spatial affinity (smoothness constraint), another important component for motion understanding, has been largely overlooked. In this paper, we introduce a novel approach, called kernel patch attention (KPA), to better resolve the ambiguity in dense matching by explicitly taking the local context relations into consideration. Our KPA operates on each local patch, and learns to mine the context affinities for better inferring the flow fields. It can be plugged into contemporary optical flow architecture and empower the model to conduct comprehensive motion analysis with both feature similarities and spatial relations. On Sintel dataset, the proposed KPA-Flow achieves the best performance with EPE of 1.35 on clean pass and 2.36 on final pass, and it sets a new record of 4.60% in F1-all on KITTI-15 benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Learning_Optical_Flow_With_Kernel_Patch_Attention_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Learning_Optical_Flow_With_Kernel_Patch_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Learning_Optical_Flow_With_Kernel_Patch_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Prompt for Open-Vocabulary Object Detection With Vision-Language Model | Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, Guoqi Li | Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as the region classifier to supervise the training of a detector. The key element that leads to the success of this model is the proper prompt, which requires careful words tuning and ingenious design. To avoid laborious prompt engineering, there are some prompt representation learning methods being proposed for the image classification task, which however can only be sub-optimal solutions when applied to the detection task. In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model. Different from the previous classification-oriented methods, DetPro has two highlights: 1) a background interpretation scheme to include the proposals in image background into the prompt training; 2) a context grading scheme to separate proposals in image foreground for tailored prompt training. We assemble DetPro with ViLD, a recent state-of-the-art openworld object detector, and conduct experiments on the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 datasets. Experimental results show that our DetPro outperforms the baseline ViLD [5] in all settings, e.g., +3.4 APbox and +3.0 APmask improvements on the novel classes of LVIS. Code and models are available at https://github.com/dyabel/detpro. | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Learning_To_Prompt_for_Open-Vocabulary_Object_Detection_With_Vision-Language_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Du_Learning_To_Prompt_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14940 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Learning_To_Prompt_for_Open-Vocabulary_Object_Detection_With_Vision-Language_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Learning_To_Prompt_for_Open-Vocabulary_Object_Detection_With_Vision-Language_Model_CVPR_2022_paper.html | CVPR 2022 | null |
TimeReplayer: Unlocking the Potential of Event Cameras for Video Interpolation | Weihua He, Kaichao You, Zhendong Qiao, Xu Jia, Ziyang Zhang, Wenhui Wang, Huchuan Lu, Yaoyuan Wang, Jianxing Liao | Recording fast motion in a high FPS (frame-per-second) requires expensive high-speed cameras. As an alternative, interpolating low-FPS videos from commodity cameras has attracted significant attention. If only low-FPS videos are available, motion assumptions (linear or quadratic) are necessary to infer intermediate frames, which fail to model complex motions. Event camera, a new camera with pixels producing events of brightness change at the temporal resolution of \mu s (10^ -6 second ), is a game-changing device to enable video interpolation at the presence of arbitrarily complex motion. Since event camera is a novel sensor, its potential has not been fulfilled due to the lack of processing algorithms. The pioneering work Time Lens introduced event cameras to video interpolation by designing optical devices to collect a large amount of paired training data of high-speed frames and events, which is too costly to scale. To fully unlock the potential of event cameras, this paper proposes a novel TimeReplayer algorithm to interpolate videos captured by commodity cameras with events. It is trained in an unsupervised cycle-consistent style, canceling the necessity of high-speed training data and bringing the additional ability of video extrapolation. Its state-of-the-art results and demo videos in supplementary reveal the promising future of event-based vision. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_TimeReplayer_Unlocking_the_Potential_of_Event_Cameras_for_Video_Interpolation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_TimeReplayer_Unlocking_the_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.13859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_TimeReplayer_Unlocking_the_Potential_of_Event_Cameras_for_Video_Interpolation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_TimeReplayer_Unlocking_the_Potential_of_Event_Cameras_for_Video_Interpolation_CVPR_2022_paper.html | CVPR 2022 | null |
General Incremental Learning With Domain-Aware Categorical Representations | Jiangwei Xie, Shipeng Yan, Xuming He | Continual learning is an important problem for achieving human-level intelligence in real-world applications as an agent must continuously accumulate knowledge in response to streaming data/tasks. In this work, we consider a general and yet under-explored incremental learning problem in which both the class distribution and class-specific domain distribution change over time. In addition to the typical challenges in class incremental learning, this setting also faces the intra-class stability-plasticity dilemma and intra-class domain imbalance problems. To address above issues, we develop a novel domain-aware continual learning method based on the EM framework. Specifically, we introduce a flexible class representation based on the von Mises-Fisher mixture model to capture the intra-class structure, using an expansion-and-reduction strategy to dynamically increase the number of components according to the class complexity. Moreover, we design a bi-level balanced memory to cope with data imbalances within and across classes, which combines with a distillation loss to achieve better inter- and intra-class stability-plasticity trade-off. We conduct exhaustive experiments on three benchmarks: iDigits, iDomainNet and iCIFAR-20. The results show that our approach consistently outperforms previous methods by a significant margin, demonstrating its superiority. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_General_Incremental_Learning_With_Domain-Aware_Categorical_Representations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_General_Incremental_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_General_Incremental_Learning_With_Domain-Aware_Categorical_Representations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_General_Incremental_Learning_With_Domain-Aware_Categorical_Representations_CVPR_2022_paper.html | CVPR 2022 | null |
Interactive Segmentation and Visualization for Tiny Objects in Multi-Megapixel Images | Chengyuan Xu, Boning Dong, Noah Stier, Curtis McCully, D. Andrew Howell, Pradeep Sen, Tobias Höllerer | We introduce an interactive image segmentation and visualization framework for identifying, inspecting, and editing tiny objects (just a few pixels wide) in large multi-megapixel high-dynamic-range (HDR) images. Detecting cosmic rays (CRs) in astronomical observations is a cumbersome workflow that requires multiple tools, so we developed an interactive toolkit that unifies model inference, HDR image visualization, segmentation mask inspection and editing into a single graphical user interface. The feature set, initially designed for astronomical data, makes this work a useful research-supporting tool for human-in-the-loop tiny-object segmentation in scientific areas like biomedicine, materials science, remote sensing, etc., as well as computer vision. Our interface features mouse-controlled, synchronized, dual-window visualization of the image and the segmentation mask, a critical feature for locating tiny objects in multi-megapixel images. The browser-based tool can be readily hosted on the web to provide multi-user access and GPU acceleration for any device. The toolkit can also be used as a high-precision annotation tool, or adapted as the frontend for an interactive machine learning framework. Our open-source dataset, CR detection model, and visualization toolkit are available at https://github.com/cy-xu/cosmic-conn. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Interactive_Segmentation_and_Visualization_for_Tiny_Objects_in_Multi-Megapixel_Images_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.10356 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Interactive_Segmentation_and_Visualization_for_Tiny_Objects_in_Multi-Megapixel_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Interactive_Segmentation_and_Visualization_for_Tiny_Objects_in_Multi-Megapixel_Images_CVPR_2022_paper.html | CVPR 2022 | null |
ActiveZero: Mixed Domain Learning for Active Stereovision With Zero Annotation | Isabella Liu, Edward Yang, Jianyu Tao, Rui Chen, Xiaoshuai Zhang, Qing Ran, Zhu Liu, Hao Su | Traditional depth sensors generate accurate real world depth estimates that surpass even the most advanced learning approaches trained only on simulation domains. Since ground truth depth is readily available in the simulation domain but quite difficult to obtain in the real domain, we propose a method that leverages the best of both worlds. In this paper we present a new framework, ActiveZero, which is a mixed domain learning solution for active stereovision systems that requires no real world depth annotation. First, we demonstrate the transferability of our method to out-of-distribution real data by using a mixed domain learning strategy. In the simulation domain, we use a combination of supervised disparity loss and self-supervised losses on a shape primitives dataset. By contrast, in the real domain, we only use self-supervised losses on a dataset that is out-of-distribution from either training simulation data or test real data. Second, our method introduces a novel self-supervised loss called temporal IR reprojection to increase the robustness and accuracy of our reprojections in hard-to-perceive regions. Finally, we show how the method can be trained end-to-end and that each module is important for attaining the end result. Extensive qualitative and quantitative evaluations on real data demonstrate state of the art results that can even beat a commercial depth sensor. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_ActiveZero_Mixed_Domain_Learning_for_Active_Stereovision_With_Zero_Annotation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_ActiveZero_Mixed_Domain_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ActiveZero_Mixed_Domain_Learning_for_Active_Stereovision_With_Zero_Annotation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_ActiveZero_Mixed_Domain_Learning_for_Active_Stereovision_With_Zero_Annotation_CVPR_2022_paper.html | CVPR 2022 | null |
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers | Xianing Chen, Qiong Cao, Yujie Zhong, Jing Zhang, Shenghua Gao, Dacheng Tao | Transformers have been successfully applied to computer vision due to its powerful modelling capacity with self-attention. However, the good performance of transformers heavily depends on enormous training images. Thus, a data-efficient transformer solution is urgently needed. In this work, we propose an early knowledge distillation framework, which is termed as DearKD, to improvethe data-efficiency required by transformers. Our DearKD is a two-stage framework that first distills the inductive biases from the early intermediate layers of a CNN and then gives the transformer full play by training without distillation. Further, our DearKD can also be applied to the extreme data-free case where no real images are available, where we propose a boundary-preserving intra-divergence loss based on DeepInversion to further close the performance gap against the full-data counterpart. Extensive experiments on ImageNet, partial ImageNet, data-free setting and other downstream tasks prove the superiority of DearKD over its baselines and state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_DearKD_Data-Efficient_Early_Knowledge_Distillation_for_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_DearKD_Data-Efficient_Early_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.12997 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_DearKD_Data-Efficient_Early_Knowledge_Distillation_for_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_DearKD_Data-Efficient_Early_Knowledge_Distillation_for_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Global-Aware Registration of Less-Overlap RGB-D Scans | Che Sun, Yunde Jia, Yi Guo, Yuwei Wu | We propose a novel method of registering less-overlap RGB-D scans. Our method learns global information of a scene to construct a panorama, and aligns RGB-D scans to the panorama to perform registration. Different from existing methods that use local feature points to register less-overlap RGB-D scans and mismatch too much, we use global information to guide the registration, thereby alleviating the mismatching problem by preserving global consistency of alignments. To this end, we build a scene inference network to construct the panorama representing global information. We introduce a reinforcement learning strategy to iteratively align RGB-D scans with the panorama and refine the panorama representation, which reduces the noise of global information and preserves global consistency of both geometric and photometric alignments. Experimental results on benchmark datasets including SUNCG, Matterport, and ScanNet show the superiority of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Global-Aware_Registration_of_Less-Overlap_RGB-D_Scans_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Global-Aware_Registration_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Global-Aware_Registration_of_Less-Overlap_RGB-D_Scans_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Global-Aware_Registration_of_Less-Overlap_RGB-D_Scans_CVPR_2022_paper.html | CVPR 2022 | null |
RayMVSNet: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo | Junhua Xi, Yifei Shi, Yijie Wang, Yulan Guo, Kai Xu | Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range (depth) finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We also devise a multi-task learning for better optimization convergence and depth accuracy. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving overall reconstruction score of 0.33mm on DTU and f-score of 59.48% on Tanks & Temples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xi_RayMVSNet_Learning_Ray-Based_1D_Implicit_Fields_for_Accurate_Multi-View_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xi_RayMVSNet_Learning_Ray-Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01320 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xi_RayMVSNet_Learning_Ray-Based_1D_Implicit_Fields_for_Accurate_Multi-View_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xi_RayMVSNet_Learning_Ray-Based_1D_Implicit_Fields_for_Accurate_Multi-View_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
ContrastMask: Contrastive Learning To Segment Every Thing | Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, Wei Shen | Partially-supervised instance segmentation is a task which requests segmenting objects from novel categories via learning on limited base categories with annotated masks thus eliminating demands of heavy annotation burden. The key to addressing this task is to build an effective class-agnostic mask segmentation model. Unlike previous methods that learn such models only on base categories, in this paper, we propose a new method, named ContrastMask, which learns a mask segmentation model on both base and novel categories under a unified pixel-level contrastive learning framework. In this framework, annotated masks of base categories and pseudo masks of novel categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa. Through this framework, feature discrimination between foreground and background is largely improved, facilitating learning of the class-agnostic mask segmentation model. Exhaustive experiments on the COCO dataset demonstrate the superiority of our method, which outperforms previous state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_ContrastMask_Contrastive_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09775 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Deep Embedded Subspace Clustering | Jinyu Cai, Jicong Fan, Wenzhong Guo, Shiping Wang, Yunhe Zhang, Zhao Zhang | Recently deep learning methods have shown significant progress in data clustering tasks. Deep clustering methods (including distance-based methods and subspace-based methods) integrate clustering and feature learning into a unified framework, where there is a mutual promotion between clustering and representation. However, deep subspace clustering methods are usually in the framework of self-expressive model and hence have quadratic time and space complexities, which prevents their applications in large-scale clustering and real-time clustering. In this paper, we propose a new mechanism for deep clustering. We aim to learn the subspace bases from deep representation in an iterative refining manner while the refined subspace bases help learning the representation of the deep neural networks in return. The proposed method is out of the self-expressive framework, scales to the sample size linearly, and is applicable to arbitrarily large datasets and online clustering scenarios. More importantly, the clustering accuracy of the proposed method is much higher than its competitors. Extensive comparison studies with state-of-the-art clustering approaches on benchmark datasets demonstrate the superiority of the proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cai_Efficient_Deep_Embedded_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture | Buzhen Huang, Liang Pan, Yuan Yang, Jingyi Ju, Yangang Wang | Due to the visual ambiguity, purely kinematic formulations on monocular human motion capture are often physically incorrect, biomechanically implausible, and can not reconstruct accurate interactions. In this work, we focus on exploiting the high-precision and non-differentiable physics simulator to incorporate dynamical constraints in motion capture. Our key-idea is to use real physical supervisions to train a target pose distribution prior for sampling-based motion control to capture physically plausible human motion. To obtain accurate reference motion with terrain interactions for the sampling, we first introduce an interaction constraint based on SDF (Signed Distance Field) to enforce appropriate ground contact modeling. We then design a novel two-branch decoder to avoid stochastic error from pseudo ground-truth and train a distribution prior with the non-differentiable physics simulator. Finally, we regress the sampling distribution from the current state of the physical character with the trained prior and sample satisfied target poses to track the estimated reference motion. Qualitative and quantitative results show that we can obtain physically plausible human motion with complex terrain interactions, human shape variations, and diverse behaviors. More information can be found at https://www.yangangwang.com/papers/HBZ-NM-2022-03.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Neural_MoCon_Neural_Motion_Control_for_Physically_Plausible_Human_Motion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Neural_MoCon_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14065 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Neural_MoCon_Neural_Motion_Control_for_Physically_Plausible_Human_Motion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Neural_MoCon_Neural_Motion_Control_for_Physically_Plausible_Human_Motion_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Temporal Alignment for Video Restoration | Kun Zhou, Wenbo Li, Liying Lu, Xiaoguang Han, Jiangbo Lu | Long-range temporal alignment is critical yet challenging for video restoration tasks. Recently, some works attempt to divide the long-range alignment into several sub-alignments and handle them progressively. Although this operation is helpful in modeling distant correspondences, error accumulation is inevitable due to the propagation mechanism. In this work, we present a novel, generic iterative alignment module which employs a gradual refinement scheme for sub-alignments, yielding more accurate motion compensation. To further enhance the alignment accuracy and temporal consistency, we develop a non-parametric re-weighting method, where the importance of each neighboring frame is adaptively evaluated in a spatial-wise way for aggregation. By virtue of the proposed strategies, our model achieves state-of-the-art performance on multiple benchmarks across a range of video restoration tasks including video super-resolution, denoising and deblurring. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Revisiting_Temporal_Alignment_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15288 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.html | CVPR 2022 | null |
Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning | Richard J. Chen, Chengkuan Chen, Yicong Li, Tiffany Y. Chen, Andrew D. Trister, Rahul G. Krishnan, Faisal Mahmood | Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e.g. - 256x256, 384x384). For gigapixel whole-slide imaging (WSI) in computational pathology, WSIs can be as large as 150000x150000 pixels at 20x magnification and exhibit a hierarchical structure of visual tokens across varying resolutions: from 16x16 images capture spatial patterns among cells, to 4096x4096 images characterizing interactions within the tissue microenvironment. We introduce a new ViT architecture called the Hierarchical Image Pyramid Transformer (HIPT), which leverages the natural hierarchical structure inherent in WSIs using two levels of self-supervised learning to learn high-resolution image representations. HIPT is pretrained across 33 cancer types using 10,678 gigapixel WSIs, 408,218 4096x4096 images, and 104M 256x256 images. We benchmark HIPT representations on 9 slide-level tasks, and demonstrate that: 1) HIPT with hierarchical pretraining outperforms current state-of-the-art methods for cancer subtyping and survival prediction, 2) self-supervised ViTs are able to model important inductive biases about the hierarchical structure of phenotypes in the tumor microenvironment. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Scaling_Vision_Transformers_to_Gigapixel_Images_via_Hierarchical_Self-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Scaling_Vision_Transformers_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Scaling_Vision_Transformers_to_Gigapixel_Images_via_Hierarchical_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Scaling_Vision_Transformers_to_Gigapixel_Images_via_Hierarchical_Self-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Reflectance for Shape Recovery With Shadow Handling | Junxuan Li, Hongdong Li | This paper aims at recovering the shape of a scene with unknown, non-Lambertian, and possibly spatially-varying surface materials. When the shape of the object is highly complex and that shadows cast on the surface, the task becomes very challenging. To overcome these challenges, we propose a coordinate-based deep MLP (multilayer perceptron) to parameterize both the unknown 3D shape and the unknown reflectance at every surface point. This network is able to leverage the observed photometric variance and shadows on the surface, and recover both surface shape and general non-Lambertian reflectance. We explicitly predict cast shadows, mitigating possible artifacts on these shadowing regions, leading to higher estimation accuracy. Our framework is entirely self-supervised, in the sense that it requires neither ground truth shape nor BRDF. Tests on real-world images demonstrate that our method outperform existing methods by a significant margin. Thanks to the small size of the MLP-net, our method is an order of magnitude faster than previous CNN-based methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Neural_Reflectance_for_Shape_Recovery_With_Shadow_Handling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Neural_Reflectance_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Neural_Reflectance_for_Shape_Recovery_With_Shadow_Handling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Neural_Reflectance_for_Shape_Recovery_With_Shadow_Handling_CVPR_2022_paper.html | CVPR 2022 | null |
Rep-Net: Efficient On-Device Learning via Feature Reprogramming | Li Yang, Adnan Siraj Rakin, Deliang Fan | Transfer learning, where the goal is to transfer the well-trained deep learning models from a primary source task to a new task, is a crucial learning scheme for on-device machine learning, due to the fact that IoT/edge devices collect and then process massive data in our daily life. However, due to the tiny memory constraint in IoT/edge devices, such on-device learning requires ultra-small training memory footprint, bringing new challenges for memory-efficient learning. Many existing works solve this problem by reducing the number of trainable parameters. However, this doesn't directly translate to memory-saving since the major bottleneck is the activations, not parameters. To develop memory-efficient on-device transfer learning, in this work, we are the first to approach the concept of transfer learning from a new perspective of intermediate feature reprogramming of a pre-trained model (i.e., backbone). To perform this lightweight and memory-efficient reprogramming, we propose to train a tiny Reprogramming Network (Rep-Net) directly from the new task input data, while freezing the backbone model. The proposed Rep-Net model interchanges the features with the backbone model using an activation connector at regular intervals to mutually benefit both the backbone model and Rep-Net model features. Through extensive experiments, we validate each design specs of the proposed Rep-Net model in achieving highly memory-efficient on-device reprogramming. Our experiments establish the superior performance (i.e., low training memory and high accuracy) of Rep-Net compared to SOTA on-device transfer learning schemes across multiple benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Rep-Net_Efficient_On-Device_Learning_via_Feature_Reprogramming_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Rep-Net_Efficient_On-Device_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Rep-Net_Efficient_On-Device_Learning_via_Feature_Reprogramming_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Rep-Net_Efficient_On-Device_Learning_via_Feature_Reprogramming_CVPR_2022_paper.html | CVPR 2022 | null |
Surface Representation for Point Clouds | Haoxi Ran, Jun Liu, Chengjie Wang | Most prior work represents the shapes of point clouds by coordinates. However, it is insufficient to describe the local geometry directly. In this paper, we present RepSurf (representative surfaces), a novel representation of point clouds to explicitly depict the very local structure. We explore two variants of RepSurf, Triangular RepSurf and Umbrella RepSurf inspired by triangle meshes and umbrella curvature in computer graphics. We compute the representations of RepSurf by predefined geometric priors after surface reconstruction. RepSurf can be a plug-and-play module for most point cloud models thanks to its free collaboration with irregular points. Based on a simple baseline of PointNet++ (SSG version), Umbrella RepSurf surpasses the previous state-of-the-art by a large margin for classification, segmentation and detection on various benchmarks in terms of performance and efficiency. With an increase of around 0.008M number of parameters, 0.04G FLOPs, and 1.12ms inference time, our method achieves 94.7% (+0.5%) on ModelNet40, and 84.6% (+1.8%) on ScanObjectNN for classification, while 74.3% (+0.8%) mIoU on S3DIS 6-fold, and 70.0% (+1.6%) mIoU on ScanNet for segmentation. For detection, previous state-of-the-art detector with our RepSurf obtains 71.2% (+2.1%) mAP_25, 54.8% (+2.0%) mAP_50 on ScanNetV2, and 64.9% (+1.9%) mAP_25, 47.1% (+2.5%) mAP_50 on SUN RGB-D. Our lightweight Triangular RepSurf performs its excellence on these benchmarks as well. The code is publicly available at https://github.com/hancyran/RepSurf. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ran_Surface_Representation_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.05740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ran_Surface_Representation_for_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
Implicit Motion Handling for Video Camouflaged Object Detection | Xuelian Cheng, Huan Xiong, Deng-Ping Fan, Yiran Zhong, Mehrtash Harandi, Tom Drummond, Zongyuan Ge | We propose a new video camouflaged object detection (VCOD) framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames. An essential property of camouflaged objects is that they usually exhibit patterns similar to the background and thus make them hard to identify from still images. Therefore, effectively handling temporal dynamics in videos becomes the key for the VCOD task as the camouflaged objects will be noticeable when they move. However, current VCOD methods often leverage homography or optical flows to represent motions, where the detection error may accumulate from both the motion estimation error and the segmentation error. On the other hand, our method unifies motion estimation and object segmentation within a single optimization framework. Specifically, we build a dense correlation volume to implicitly capture motions between neighbouring frames and utilize the final segmentation supervision to optimize the implicit motion estimation and segmentation jointly. Furthermore, to enforce temporal consistency within a video sequence, we jointly utilize a spatio-temporal transformer to refine the short-term predictions. Extensive experiments on VCOD benchmarks demonstrate the architectural effectiveness of our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD benchmark with previous methods to facilitate research in this direction. Dataset Link: https://xueliancheng.github.io/SLT-Net-project. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Implicit_Motion_Handling_for_Video_Camouflaged_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cheng_Implicit_Motion_Handling_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07363 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Implicit_Motion_Handling_for_Video_Camouflaged_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Implicit_Motion_Handling_for_Video_Camouflaged_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
OVE6D: Object Viewpoint Encoding for Depth-Based 6D Object Pose Estimation | Dingding Cai, Janne Heikkilä, Esa Rahtu | This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask. Our model is trained using purely synthetic data rendered from ShapeNet, and, unlike most of the existing methods, it generalizes well on new real-world objects without any fine-tuning. We achieve this by decomposing the 6D pose into viewpoint, in-plane rotation around the camera optical axis and translation, and introducing novel lightweight modules for estimating each component in a cascaded manner. The resulting network contains less than 4M parameters while demonstrating excellent performance on the challenging T-LESS and Occluded LINEMOD datasets without any dataset-specific training. We show that OVE6D outperforms some contemporary deep learning-based pose estimation methods specifically trained for individual objects or datasets with real-world training data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_OVE6D_Object_Viewpoint_Encoding_for_Depth-Based_6D_Object_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cai_OVE6D_Object_Viewpoint_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_OVE6D_Object_Viewpoint_Encoding_for_Depth-Based_6D_Object_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_OVE6D_Object_Viewpoint_Encoding_for_Depth-Based_6D_Object_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
DeepLIIF: An Online Platform for Quantification of Clinical Pathology Slides | Parmida Ghahremani, Joseph Marino, Ricardo Dodds, Saad Nadeem | In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin (H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists on glass slides or as digital scans for diagnosis and assessment of disease progression. Cell-level quantification, e.g. in IHC protein expression scoring, can be extremely inefficient and subjective. We present DeepLIIF (https://deepliif.org), a first free online platform for efficient and reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art approaches (relying on manual error-prone annotations) by virtually restaining clinical IHC slides with more informative multiplex immunofluorescence staining. Our DeepLIIF cloud-native platform supports (1) more than 150 proprietary/non-proprietary input formats via the Bio-Formats standard, (2) interactive adjustment, visualization, and downloading of the IHC quantification results and the accompanying restained images, (3) consumption of an exposed workflow API programmatically or through interactive plugins for open source whole slide image viewers such as QuPath/ImageJ, and (4) auto scaling to efficiently scale GPU resources based on user demand. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ghahremani_DeepLIIF_An_Online_Platform_for_Quantification_of_Clinical_Pathology_Slides_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.04494 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ghahremani_DeepLIIF_An_Online_Platform_for_Quantification_of_Clinical_Pathology_Slides_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ghahremani_DeepLIIF_An_Online_Platform_for_Quantification_of_Clinical_Pathology_Slides_CVPR_2022_paper.html | CVPR 2022 | null |
Joint Video Summarization and Moment Localization by Cross-Task Sample Transfer | Hao Jiang, Yadong Mu | Video summarization has recently engaged increasing attention in computer vision communities. However, the scarcity of annotated data has been a key obstacle in this task. To address it, this work explores a new solution for video summarization by transferring samples from a correlated task (i.e., video moment localization) equipped with abundant training data. Our main insight is that the annotated video moments also indicate the semantic highlights of a video, essentially similar to video summary. Approximately, the video summary can be treated as a sparse, redundancy-free version of the video moments. Inspired by this observation, we propose an importance Propagation based collaborative Teaching Network (iPTNet). It consists of two separate modules that conduct video summarization and moment localization, respectively. Each module estimates a frame-wise importance map for indicating keyframes or moments. To perform cross-task sample transfer, we devise an importance propagation module that realizes the conversion between summarization-guided and localization-guided importance maps. This way critically enables optimizing one of the tasks using the data from the other task. Additionally, in order to avoid error amplification caused by batch-wise joint training, we devise a collaborative teaching scheme, which adopts a cross-task mean teaching strategy to realize the joint optimization of the two tasks and provide robust frame-level teaching signals. Extensive experiments on video summarization benchmarks demonstrate that iPTNet significantly outperforms previous state-of-the-art video summarization methods, serving as an effective solution that overcomes the data scarcity issue in video summarization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Joint_Video_Summarization_and_Moment_Localization_by_Cross-Task_Sample_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Joint_Video_Summarization_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Joint_Video_Summarization_and_Moment_Localization_by_Cross-Task_Sample_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Joint_Video_Summarization_and_Moment_Localization_by_Cross-Task_Sample_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery | N. Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan | Current methods for object detection, segmentation, and tracking fail in the presence of severe occlusions in busy urban environments. Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions. In this work, we present the best of both the real and synthetic worlds for automatic occlusion supervision using a large readily available source of data: time-lapse imagery from stationary webcams observing street intersections over weeks, months, or even years. We introduce a new dataset, Watch and Learn Time-lapse (WALT), consisting of 12 (4K and 1080p) cameras capturing urban environments over a year. We exploit this real data in a novel way to automatically mine a large set of unoccluded objects and then composite them in the same views to generate occlusions. This longitudinal self-supervision is strong enough for an amodal network to learn object-occluder-occluded layer representations. We show how to speed up the discovery of unoccluded objects and relate the confidence in this discovery to the rate and accuracy of training occluded objects. After watching and automatically learning for several days, this approach shows significant performance improvement in detecting and segmenting occluded people and vehicles, over human-supervised amodal approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Reddy_WALT_Watch_and_Learn_2D_Amodal_Representation_From_Time-Lapse_Imagery_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Reddy_WALT_Watch_and_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Reddy_WALT_Watch_and_Learn_2D_Amodal_Representation_From_Time-Lapse_Imagery_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Reddy_WALT_Watch_and_Learn_2D_Amodal_Representation_From_Time-Lapse_Imagery_CVPR_2022_paper.html | CVPR 2022 | null |
Learning With Twin Noisy Labels for Visible-Infrared Person Re-Identification | Mouxing Yang, Zhenyu Huang, Peng Hu, Taihao Li, Jiancheng Lv, Xi Peng | In this paper, we study an untouched problem in visible-infrared person re-identification (VI-ReID), namely, Twin Noise Labels (TNL) which refers to as noisy annotation and correspondence. In brief, on the one hand, it is inevitable to annotate some persons with the wrong identity due to the complexity in data collection and annotation, e.g., the poor recognizability in the infrared modality. On the other hand, the wrongly annotated data in a single modality will eventually contaminate the cross-modal correspondence, thus leading to noisy correspondence. To solve the TNL problem, we propose a novel method for robust VI-ReID, termed DuAlly Robust Training (DART). In brief, DART first computes the clean confidence of annotations by resorting to the memorization effect of deep neural networks. Then, the proposed method rectifies the noisy correspondence with the estimated confidence and further divides the data into four groups for further utilizations. Finally, DART employs a novel dually robust loss consisting of a soft identification loss and an adaptive quadruplet loss to achieve robustness on the noisy annotation and noisy correspondence. Extensive experiments on SYSU-MM01 and RegDB datasets verify the effectiveness of our method against the twin noisy labels compared with five state-of-the-art methods. The code could be accessed from https://github.com/XLearning-SCU/2022-CVPR-DART. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Learning_With_Twin_Noisy_Labels_for_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Learning_With_Twin_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Learning_With_Twin_Noisy_Labels_for_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Learning_With_Twin_Noisy_Labels_for_Visible-Infrared_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Optical Flow Estimation for Spiking Camera | Liwen Hu, Rui Zhao, Ziluo Ding, Lei Ma, Boxin Shi, Ruiqin Xiong, Tiejun Huang | As a bio-inspired sensor with high temporal resolution, the spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. However, frame-based and event-based methods are not well suited to spike streams from the spiking camera due to the different data modalities. To this end, we present, SCFlow, a tailored deep learning pipeline to estimate optical flow in high-speed scenes from spike streams. Importantly, a novel input representation is introduced which can adaptively remove the motion blur in spike streams according to the prior motion. Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes. Experimental results show that the SCFlow can predict optical flow from spike streams in different high-speed scenes. Moreover, SCFlow shows promising generalization on real spike streams. Codes and datasets refer to https://github.com/Acnext/Optical-Flow-For-Spiking-Camera. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Optical_Flow_Estimation_for_Spiking_Camera_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Optical_Flow_Estimation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.03916 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Optical_Flow_Estimation_for_Spiking_Camera_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Optical_Flow_Estimation_for_Spiking_Camera_CVPR_2022_paper.html | CVPR 2022 | null |
MetaFormer Is Actually What You Need for Vision | Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan | Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 49%/61% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_MetaFormer_Is_Actually_What_You_Need_for_Vision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_MetaFormer_Is_Actually_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.11418 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_MetaFormer_Is_Actually_What_You_Need_for_Vision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_MetaFormer_Is_Actually_What_You_Need_for_Vision_CVPR_2022_paper.html | CVPR 2022 | null |
GradViT: Gradient Inversion of Vision Transformers | Ali Hatamizadeh, Hongxu Yin, Holger R. Roth, Wenqi Li, Jan Kautz, Daguang Xu, Pavlo Molchanov | In this work we demonstrate the vulnerability of vision transformers (ViTs) to gradient-based inversion attacks. During this attack, the original data batch is reconstructed given model weights and the corresponding gradients. We introduce a method, named GradViT, that optimizes random noise into naturally looking images via an iterative process. The optimization objective consists of (i) a loss on matching the gradients, (ii) image prior in the form of distance to batch normalization statistics of a pretrained CNN model, and (iii) a total variation regularization on patches to guide correct recovery locations. We propose a unique loss scheduling function to overcome local minima during optimization. We evaluate GadViT on ImageNet1K and MS-Celeb-1M datasets, and observe unprecedentedly high fidelity and closeness to the original (hidden) data. During the analysis we find that vision transformers are significantly more vulnerable than previously studied CNNs due to the presence of the attention mechanism. Our method demonstrates new state-of-the-art results for gradient inversion in both qualitative and quantitative metrics. Project page at https://gradvit.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hatamizadeh_GradViT_Gradient_Inversion_of_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hatamizadeh_GradViT_Gradient_Inversion_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.11894 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hatamizadeh_GradViT_Gradient_Inversion_of_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hatamizadeh_GradViT_Gradient_Inversion_of_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning | Mengshun Hu, Kui Jiang, Liang Liao, Jing Xiao, Junjun Jiang, Zheng Wang | Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate super-resolved videos with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution (T-VSR) but ignore the reciprocal relations among them. Specifically, 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation with more clues; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. To this end, we propose a one-stage based Cycle-projected Mutual learning network (CycMu-Net) for ST-VSR, which makes full use of spatial-temporal correlations via the mutual learning between S-VSR and T-VSR. Specifically, we propose to exploit the mutual information among them via iterative up-and-down projections, where the spatial and temporal features are fully fused and distilled, helping the high-quality video reconstruction. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMu-Net with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms state-of-the-art methods. Codes are publicly available at: https://github.com/hhhhhumengshun/CycMuNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Spatial-Temporal_Space_Hand-in-Hand_Spatial-Temporal_Video_Super-Resolution_via_Cycle-Projected_Mutual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
InstaFormer: Instance-Aware Image-to-Image Translation With Transformer | Soohyun Kim, Jongbeom Baek, Jihye Park, Gyeongnyeon Kim, Seungryong Kim | We present a novel Transformer-based network architecture for instance-aware image-to-image translation, dubbed InstaFormer, to effectively integrate global- and instance-level information. By considering extracted content features from an image as tokens, our networks discover global consensus of content features by considering context information through a self-attention module in Transformers. By augmenting such tokens with an instance-level feature extracted from the content feature with respect to bounding box information, our framework is capable of learning an interaction between object instances and the global image, thus boosting the instance-awareness. We replace layer normalization (LayerNorm) in standard Transformers with adaptive instance normalization (AdaIN) to enable a multi-modal translation with style codes. In addition, to improve the instance-awareness and translation quality at object regions, we present an instance-level content contrastive loss defined between input and translated image. We conduct experiments to demonstrate the effectiveness of our InstaFormer over the latest methods and provide extensive ablation studies. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_InstaFormer_Instance-Aware_Image-to-Image_Translation_With_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_InstaFormer_Instance-Aware_Image-to-Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16248 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_InstaFormer_Instance-Aware_Image-to-Image_Translation_With_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_InstaFormer_Instance-Aware_Image-to-Image_Translation_With_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Near/Remote Sensing With Geospatial Attention | Scott Workman, M. Usman Rafique, Hunter Blanton, Nathan Jacobs | This work addresses the task of overhead image segmentation when auxiliary ground-level images are available. Recent work has shown that performing joint inference over these two modalities, often called near/remote sensing, can yield significant accuracy improvements. Extending this line of work, we introduce the concept of geospatial attention, a geometry-aware attention mechanism that explicitly considers the geospatial relationship between the pixels in a ground-level image and a geographic location. We propose an approach for computing geospatial attention that incorporates geometric features and the appearance of the overhead and ground-level imagery. We introduce a novel architecture for near/remote sensing that is based on geospatial attention and demonstrate its use for five segmentation tasks. The results demonstrate that our method significantly outperforms the previous state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Workman_Revisiting_NearRemote_Sensing_With_Geospatial_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Workman_Revisiting_NearRemote_Sensing_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01807 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Workman_Revisiting_NearRemote_Sensing_With_Geospatial_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Workman_Revisiting_NearRemote_Sensing_With_Geospatial_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Joint Global and Local Hierarchical Priors for Learned Image Compression | Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee | Recently, learned image compression methods have outperformed traditional hand-crafted ones including BPG. One of the keys to this success is learned entropy models that estimate the probability distribution of the quantized latent representation. Like other vision tasks, most recent learned entropy models are based on convolutional neural networks (CNNs). However, CNNs have a limitation in modeling long-range dependencies due to their nature of local connectivity, which can be a significant bottleneck in image compression where reducing spatial redundancy is a key point. To overcome this issue, we propose a novel entropy model called Information Transformer (Informer) that exploits both global and local information in a content-dependent manner using an attention mechanism. Our experiments show that Informer improves rate-distortion performance over the state-of-the-art methods on the Kodak and Tecnick datasets without the quadratic computational complexity problem. Our source code is available at https://github.com/naver-ai/informer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Joint_Global_and_Local_Hierarchical_Priors_for_Learned_Image_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Joint_Global_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04487 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Joint_Global_and_Local_Hierarchical_Priors_for_Learned_Image_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Joint_Global_and_Local_Hierarchical_Priors_for_Learned_Image_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
Knowledge Distillation via the Target-Aware Transformer | Sihao Lin, Hongwei Xie, Bing Wang, Kaicheng Yu, Xiaojun Chang, Xiaodan Liang, Gang Wang | Knowledge distillation becomes a de facto standard to improve the performance of small neural networks. Most of the previous works propose to regress the representational features from the teacher to the student in a one-to-one spatial matching fashion. However, people tend to overlook the fact that, due to the architecture differences, the semantic information on the same spatial location usually vary. This greatly undermines the underlying assumption of the one-to-one distillation approach. To this end, we propose a novel one-to-all spatial matching knowledge distillation approach. Specifically, we allow each pixel of the teacher feature to be distilled to all spatial locations of the student features given its similarity, which is generated from a target-aware transformer. Our approach surpasses the state-of-the-art methods by a significant margin on various computer vision benchmarks, such as ImageNet, Pascal VOC and COCOStuff10k. Code is available at https://github.com/sihaoevery/TaT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Knowledge_Distillation_via_the_Target-Aware_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Knowledge_Distillation_via_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.10793 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Knowledge_Distillation_via_the_Target-Aware_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Knowledge_Distillation_via_the_Target-Aware_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Recurring the Transformer for Video Action Recognition | Jiewen Yang, Xingbo Dong, Liujun Liu, Chao Zhang, Jiajun Shen, Dahai Yu | Existing video understanding approaches, such as 3D convolutional neural networks and Transformer-Based methods, usually process the videos in a clip-wise manner. Hence huge GPU memory is needed, and fixed-length video clips are usually required. We introduce a novel Recurrent Vision Transformer (RViT) framework for spatial-temporal representation learning to achieve the video action recognition task. Specifically, the proposed RViT is equipped with an attention gate which is utilized to build interaction between current frame input and previous hidden state, thus aggregating the global level inter-frame features through the hidden state. RViT is executed recurrently to process a video clip by giving the current frame and previous hidden state. The RViT can capture both spatial and temporal features because of the attention gate and recurrent execution. Besides, the proposed RViT can work on both fixed-length and variant-length video clips properly without requiring large GPU memory thanks to the frame by frame processing flow. Our experiment results verify that RViT can achieve state-of-the-art performance on various datasets for the video recognition task. Specifically, RViT can achieve a top-1 accuracy of 81.5% on Kinetics-400, 92.31% on Jester, 67.9% on Something-Something-V2, and an mAP accuracy of 66.1% on Charades. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Recurring_the_Transformer_for_Video_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Recurring_the_Transformer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Recurring_the_Transformer_for_Video_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Recurring_the_Transformer_for_Video_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Subspace Adversarial Training | Tao Li, Yingwen Wu, Sizhe Chen, Kun Fang, Xiaolin Huang | Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However, a serious problem of catastrophic overfitting exists, i.e., the robust accuracy against projected gradient descent (PGD) attack suddenly drops to 0% during the training. In this paper, we approach this problem from a novel perspective of optimization and firstly reveal the close link between the fast-growing gradient of each sample and overfitting, which can also be applied to understand robust overfitting in multi-step AT. To control the growth of the gradient, we propose a new AT method, Subspace Adversarial Training (Sub-AT), which constrains AT in a carefully extracted subspace. It successfully resolves both kinds of overfitting and significantly boosts the robustness. In subspace, we also allow single-step AT with larger steps and larger radius, further improving the robustness performance. As a result, we achieve state-of-the-art single-step AT performance. Without any regularization term, our single-step AT can reach over 51% robust accuracy against strong PGD-50 attack of radius 8/255 on CIFAR-10, reaching a competitive performance against standard multi-step PGD-10 AT with huge computational advantages. The code is released at https://github.com/nblt/Sub-AT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Subspace_Adversarial_Training_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Subspace_Adversarial_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12229 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Subspace_Adversarial_Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Subspace_Adversarial_Training_CVPR_2022_paper.html | CVPR 2022 | null |
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection | Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt, Mohammad-Ali Nikouei Mahani, Nassir Navab, Benjamin Busam, Federico Tombari | As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, in safety-critical settings, robustness to out-of-domain and long-tail samples is fundamental to circumvent dangerous issues, such as the misdetection of damaged or rare cars. In this work, we substantially improve the generalization of 3D object detectors to out-of-domain data by deforming point clouds during training. We achieve this with 3D-VField: a novel data augmentation method that plausibly deforms objects via vector fields learned in an adversarial fashion. Our approach constrains 3D points to slide along their sensor view rays while neither adding nor removing any of them. The obtained vectors are transferable, sample-independent and preserve shape and occlusions. Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes. Towards this end, we propose and share CrashD: a synthetic dataset of realistic damaged and rare cars, with a variety of crash scenarios. Extensive experiments on KITTI, Waymo, our CrashD and SUN RGB-D show the generalizability of our techniques to out-of-domain data, different models and sensors, namely LiDAR and ToF cameras, for both indoor and outdoor scenes. Our CrashD dataset is available at https://crashd-cars.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lehner_3D-VField_Adversarial_Augmentation_of_Point_Clouds_for_Domain_Generalization_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lehner_3D-VField_Adversarial_Augmentation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lehner_3D-VField_Adversarial_Augmentation_of_Point_Clouds_for_Domain_Generalization_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lehner_3D-VField_Adversarial_Augmentation_of_Point_Clouds_for_Domain_Generalization_in_CVPR_2022_paper.html | CVPR 2022 | null |
Image Segmentation Using Text and Image Prompts | Timo Lüddecke, Alexander Ecker | Image segmentation is usually addressed by training a model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system that can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text or an image. This approach enables us to create a unified model (trained once) for three common segmentation tasks, which come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation. We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense prediction. After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail. This novel hybrid input allows for dynamic adaptation not only to the three segmentation tasks mentioned above, but to any binary segmentation task where a text or image query can be formulated. Finally, we find our system to adapt well to generalized queries involving affordances or properties. Code is available at https://eckerlab.org/code/clipseg | https://openaccess.thecvf.com/content/CVPR2022/papers/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luddecke_Image_Segmentation_Using_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luddecke_Image_Segmentation_Using_Text_and_Image_Prompts_CVPR_2022_paper.html | CVPR 2022 | null |
AutoMine: An Unmanned Mine Dataset | Yuchen Li, Zixuan Li, Siyu Teng, Yu Zhang, Yuhang Zhou, Yuchang Zhu, Dongpu Cao, Bin Tian, Yunfeng Ai, Zhe Xuanyuan, Long Chen | Autonomous driving datasets have played an important role in validating the advancement of intelligent vehicle algorithms including localization, perception and prediction in academic areas. However, current existing datasets pay more attention to the structured urban road, which hampers the exploration on unstructured special scenarios. Moreover, the open-pit mine is one of the typical representatives for them. Therefore, we introduce the Autonomous driving dataset on the Mining scene (AutoMine) for positioning and perception tasks in this paper. The AutoMine is collected by multiple acquisition platforms including an SUV, a wide-body mining truck and an ordinary mining truck, depending on the actual mine operation scenarios. The dataset consists of 18+ driving hours, 18K annotated lidar and image frames for 3D perception with various mines, time-of-the-day and weather conditions. The main contributions of the AutoMine dataset are as follows: 1.The first autonomous driving dataset for perception and localization in mine scenarios. 2.There are abundant dynamic obstacles of 9 degrees of freedom with large dimension difference (mining trucks and pedestrians) and extreme climatic conditions (the dust and snow) in the mining area. 3.Multi-platform acquisition strategies could capture mining data from multiple perspectives that fit the actual operation. More details can be found in our website(https://automine.cc). | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_AutoMine_An_Unmanned_Mine_Dataset_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_AutoMine_An_Unmanned_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_AutoMine_An_Unmanned_Mine_Dataset_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_AutoMine_An_Unmanned_Mine_Dataset_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Data-Dependent Transform for Learned Image Compression | Dezhao Wang, Wenhan Yang, Yueyu Hu, Jiaying Liu | Learned image compression has achieved great success due to its excellent modeling capacity, but seldom further considers the Rate-Distortion Optimization (RDO) of each input image. To explore this potential in the learned codec, we make the first attempt to build a neural data-dependent transform and introduce a continuous online mode decision mechanism to jointly optimize the coding efficiency for each individual image. Specifically, apart from the image content stream, we employ an additional model stream to generate the transform parameters at the decoder side. The presence of a model stream enables our model to learn more abstract neural-syntax, which helps cluster the latent representations of images more compactly. Beyond the transform stage, we also adopt neural-syntax based post-processing for the scenarios that require higher quality reconstructions regardless of extra decoding overhead. Moreover, the involvement of the model stream further makes it possible to optimize both the representation and the decoder in an online way, i.e. RDO at the testing time. It is equivalent to a continuous online mode decision, like coding modes in the traditional codecs, to improve the coding efficiency based on the individual input image. The experimental results show the effectiveness of the proposed neural-syntax design and the continuous online mode decision mechanism, demonstrating the superiority of our method in coding efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Neural_Data-Dependent_Transform_for_Learned_Image_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Neural_Data-Dependent_Transform_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04963 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Neural_Data-Dependent_Transform_for_Learned_Image_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Neural_Data-Dependent_Transform_for_Learned_Image_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
Background Activation Suppression for Weakly Supervised Object Localization | Pingyu Wu, Wei Zhai, Yang Cao | Weakly supervised object localization (WSOL) aims to localize objects using only image-level labels. Recently a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve localization task. Existing FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction map and to guide the learning of generator. We argue for using activation value to achieve more efficient learning. It is based on the experimental observation that, for a trained network, CE converges to zero when the foreground mask covers only part of the object region. While activation value increases until the mask expands to the object boundary, which indicates that more object areas can be learned by using activation value. In this paper, we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint module (AMC) is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using the foreground region guidance and the area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets. Code and models are available at https://github.com/wpy1999/BAS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Background_Activation_Suppression_for_Weakly_Supervised_Object_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Background_Activation_Suppression_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00580 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Background_Activation_Suppression_for_Weakly_Supervised_Object_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Background_Activation_Suppression_for_Weakly_Supervised_Object_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
How Many Observations Are Enough? Knowledge Distillation for Trajectory Forecasting | Alessio Monti, Angelo Porrello, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara | Accurate prediction of future human positions is an essential task for modern video-surveillance systems. Current state-of-the-art models usually rely on a "history" of past tracked locations (e.g., 3 to 5 seconds) to predict a plausible sequence of future locations (e.g., up to the next 5 seconds). We feel that this common schema neglects critical traits of realistic applications: as the collection of input trajectories involves machine perception (i.e., detection and tracking), incorrect detection and fragmentation errors may accumulate in crowded scenes, leading to tracking drifts. On this account, the model would be fed with corrupted and noisy input data, thus fatally affecting its prediction performance. In this regard, we focus on delivering accurate predictions when only a few input observations are used, thus potentially lowering the risks associated with automatic perception. To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones). We show that a properly defined teacher supervision allows a student network to perform comparably to state-of-the-art approaches that demand more observations. Besides, extensive experiments on common trajectory forecasting datasets highlight that our student network better generalizes to unseen scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Monti_How_Many_Observations_Are_Enough_Knowledge_Distillation_for_Trajectory_Forecasting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Monti_How_Many_Observations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04781 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Monti_How_Many_Observations_Are_Enough_Knowledge_Distillation_for_Trajectory_Forecasting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Monti_How_Many_Observations_Are_Enough_Knowledge_Distillation_for_Trajectory_Forecasting_CVPR_2022_paper.html | CVPR 2022 | null |
Evaluation-Oriented Knowledge Distillation for Deep Face Recognition | Yuge Huang, Jiaxiang Wu, Xingkun Xu, Shouhong Ding | Knowledge distillation (KD) is a widely-used technique that utilizes large networks to improve the performance of compact models. Previous KD approaches usually aim to guide the student to mimic the teacher's behavior completely in the representation space. However, such one-to-one corresponding constraints may lead to inflexible knowledge transfer from the teacher to the student, especially those with low model capacities. Inspired by the ultimate goal of KD methods, we propose a novel Evaluation oriented KD method (EKD) for deep face recognition to directly reduce the performance gap between the teacher and student models during training. Specifically, we adopt the commonly used evaluation metrics in face recognition, i.e., False Positive Rate (FPR) and True Positive Rate (TPR) as the performance indicator. According to the evaluation protocol, the critical pair relations that cause the TPR and FPR difference between the teacher and student models are selected. Then, the critical relations in the student are constrained to approximate the corresponding ones in the teacher by a novel rank-based loss function, giving more flexibility to the student with low capacity. Extensive experimental results on popular benchmarks demonstrate the superiority of our EKD over state-of-the-art competitors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Evaluation-Oriented_Knowledge_Distillation_for_Deep_Face_Recognition_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Evaluation-Oriented_Knowledge_Distillation_for_Deep_Face_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Evaluation-Oriented_Knowledge_Distillation_for_Deep_Face_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Improving Subgraph Recognition With Variational Graph Information Bottleneck | Junchi Yu, Jie Cao, Ran He | Subgraph recognition aims at discovering a compressed substructure of a graph that is most informative to the graph property. It can be formulated by optimizing Graph Information Bottleneck (GIB) with a mutual information estimator. However, GIB suffers from training instability and degenerated results due to its intrinsic optimization process. To tackle these issues, we reformulate the subgraph recognition problem into two steps: graph perturbation and subgraph selection, leading to a novel Variational Graph Information Bottleneck (VGIB) framework. VGIB first employs the noise injection to modulate the information flow from the input graph to the perturbed graph. Then, the perturbed graph is encouraged to be informative to the graph property. VGIB further obtains the desired subgraph by filtering out the noise in the perturbed graph. With the customized noise prior for each input, the VGIB objective is endowed with a tractable variational upper bound, leading to a superior empirical performance as well as theoretical properties. Extensive experiments on graph interpretation, explainability of Graph Neural Networks, and graph classification show that VGIB finds better subgraphs than existing methods Extensive experiments on the explainability of Graph Neural Networks, graph interpretation, and graph classification show that VGIB finds better subgraphs than existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Improving_Subgraph_Recognition_With_Variational_Graph_Information_Bottleneck_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.09899 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Improving_Subgraph_Recognition_With_Variational_Graph_Information_Bottleneck_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Improving_Subgraph_Recognition_With_Variational_Graph_Information_Bottleneck_CVPR_2022_paper.html | CVPR 2022 | null |
Slot-VPS: Object-Centric Representation Learning for Video Panoptic Segmentation | Yi Zhou, Hui Zhang, Hana Lee, Shuyang Sun, Pingjun Li, Yangguang Zhu, ByungIn Yoo, Xiaojuan Qi, Jae-Joon Han | Video Panoptic Segmentation (VPS) aims at assigning a class label to each pixel, uniquely segmenting and identifying all object instances consistently across all frames. Classic solutions usually decompose the VPS task into several sub-tasks and utilize multiple surrogates (e.g. boxes and masks, centers and offsets) to represent objects. However, this divide-and-conquer strategy requires complex post-processing in both spatial and temporal domains and is vulnerable to failures from surrogate tasks. In this paper, inspired by object-centric learning which learns compact and robust object representations, we present Slot-VPS, the first end-to-end framework for this task. We encode all panoptic entities in a video, including both foreground instances and background semantics, in a unified representation called panoptic slots. The coherent spatio-temporal object's information is retrieved and encoded into the panoptic slots by the proposed Video Panoptic Retriever, enabling to localize, segment, differentiate, and associate objects in a unified manner. Finally, the output panoptic slots can be directly converted into the class, mask, and object ID of panoptic objects in the video. We conduct extensive ablation studies and demonstrate the effectiveness of our approach on two benchmark datasets, Cityscapes-VPS (val and test sets) and VIPER (val set), achieving new state-of-the-art performance of 63.7, 63.3 and 56.2 VPQ, respectively. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Slot-VPS_Object-Centric_Representation_Learning_for_Video_Panoptic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Slot-VPS_Object-Centric_Representation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Slot-VPS_Object-Centric_Representation_Learning_for_Video_Panoptic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Slot-VPS_Object-Centric_Representation_Learning_for_Video_Panoptic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Motion-From-Blur: 3D Shape and Motion Estimation of Motion-Blurred Objects in Videos | Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys | We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to estimate all parameters by minimizing the pixel-wise reprojection error to the input video via backpropagating through a rendering pipeline that accounts for motion blur by averaging the graphics output over short time intervals. For that purpose, we also estimate the camera exposure gap time within the same optimization. To account for abrupt motion changes like bounces, we model the motion trajectory as a piece-wise polynomial, and we are able to estimate the specific time of the bounce at sub-frame accuracy. Experiments on established benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rozumnyi_Motion-From-Blur_3D_Shape_and_Motion_Estimation_of_Motion-Blurred_Objects_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rozumnyi_Motion-From-Blur_3D_Shape_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rozumnyi_Motion-From-Blur_3D_Shape_and_Motion_Estimation_of_Motion-Blurred_Objects_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rozumnyi_Motion-From-Blur_3D_Shape_and_Motion_Estimation_of_Motion-Blurred_Objects_in_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Video Instance Segmentation via Tracklet Query and Proposal | Jialian Wu, Sudhir Yarram, Hui Liang, Tian Lan, Junsong Yuan, Jayan Eledath, Gérard Medioni | Video Instance Segmentation (VIS) aims to simultaneously classify, segment, and track multiple object instances in videos. Recent clip-level VIS takes a short video clip as input each time showing stronger performance than frame-level VIS (tracking-by-segmentation), as more temporal context from multiple frames is utilized. Yet, most clip-level methods are neither end-to-end learnable nor real-time. These limitations are addressed by the recent VIS transformer (VisTR) which performs VIS end-to-end within a clip. However, VisTR suffers from long training time due to its frame-wise dense attention. In addition, VisTR is not fully end-to-end learnable in multiple video clips as it requires a hand-crafted data association to link instance tracklets between successive clips. This paper proposes EfficientVIS, a fully end-to-end framework with efficient training and inference. At the core are tracklet query and tracklet proposal that associate and segment regions-of-interest (RoIs) across space and time by an iterative query-video interaction. We further propose a correspondence learning that makes tracklets linking between clips end-to-end learnable. Compared to VisTR, EfficientVIS requires 15x fewer training epochs while achieving state-of-the-art accuracy on the YouTube-VIS benchmark. Meanwhile, our method enables whole video instance segmentation in a single end-to-end pass without data association at all. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Efficient_Video_Instance_Segmentation_via_Tracklet_Query_and_Proposal_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Efficient_Video_Instance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01853 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Efficient_Video_Instance_Segmentation_via_Tracklet_Query_and_Proposal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Efficient_Video_Instance_Segmentation_via_Tracklet_Query_and_Proposal_CVPR_2022_paper.html | CVPR 2022 | null |
Synthetic Generation of Face Videos With Plethysmograph Physiology | Zhen Wang, Yunhao Ba, Pradyumna Chari, Oyku Deniz Bozkurt, Gianna Brown, Parth Patwa, Niranjan Vaddi, Laleh Jalilian, Achuta Kadambi | Accelerated by telemedicine, advances in Remote Photoplethysmography (rPPG) are beginning to offer a viable path toward non-contact physiological measurement. Unfortunately, the datasets for rPPG are limited as they require videos of the human face paired with ground-truth, synchronized heart rate data from a medical-grade health monitor. Also troubling is that the datasets are not inclusive of diverse populations, i.e., current real rPPG facial video datasets are imbalanced in terms of races or skin tones, leading to accuracy disparities on different demographic groups. This paper proposes a scalable biophysical learning based method to generate physio-realistic synthetic rPPG videos given any reference image and target rPPG signal and shows that it could further improve the state-of-the-art physiological measurement and reduce the bias among different groups. We also collect the largest rPPG dataset of its kind (UCLA-rPPG) with a diverse presence of subject skin tones, in the hope that this could serve as a benchmark dataset for different skin tones in this area and ensure that advances of the technique can benefit all people for healthcare equity. The dataset is available at https://visual.ee.ucla.edu/rppg_avatars.htm/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Synthetic_Generation_of_Face_Videos_With_Plethysmograph_Physiology_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Synthetic_Generation_of_Face_Videos_With_Plethysmograph_Physiology_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Synthetic_Generation_of_Face_Videos_With_Plethysmograph_Physiology_CVPR_2022_paper.html | CVPR 2022 | null |
TransRAC: Encoding Multi-Scale Temporal Correlation With Transformers for Repetitive Action Counting | Huazhang Hu, Sixun Dong, Yiqun Zhao, Dongze Lian, Zhengxin Li, Shenghua Gao | Counting repetitive actions are widely seen in human activities such as physical exercise. Existing methods focus on performing repetitive action counting in short videos, which is tough for dealing with longer videos in more realistic scenarios. In the data-driven era, the degradation of such generalization capability is mainly attributed to the lack of long video datasets. To complement this margin, we introduce a new large-scale repetitive action counting dataset covering a wide variety of video lengths, along with more realistic situations where action interruption or action inconsistencies occur in the video. Besides, we also provide a fine-grained annotation of the action cycles instead of just counting annotation along with a numerical value. Such a dataset contains 1,451 videos with about 20,000 annotations, which is more challenging. For repetitive action counting towards more realistic scenarios, we further propose encoding multi-scale temporal correlation with transformers that can take into account both performance and efficiency. Furthermore, with the help of fine-grained annotation of action cycles, we propose a density map regression-based method to predict the action period, which yields better performance with sufficient interpretability. Our proposed method outperforms state-of-the-art methods on all datasets and also achieves better performance on the unseen dataset without fine-tuning. The dataset and code are available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_TransRAC_Encoding_Multi-Scale_Temporal_Correlation_With_Transformers_for_Repetitive_Action_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_TransRAC_Encoding_Multi-Scale_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01018 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_TransRAC_Encoding_Multi-Scale_Temporal_Correlation_With_Transformers_for_Repetitive_Action_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_TransRAC_Encoding_Multi-Scale_Temporal_Correlation_With_Transformers_for_Repetitive_Action_CVPR_2022_paper.html | CVPR 2022 | null |
Hallucinated Neural Radiance Fields in the Wild | Xingyu Chen, Qi Zhang, Xiaoyu Li, Yue Chen, Ying Feng, Xuan Wang, Jue Wang | Neural Radiance Fields (NeRF) has recently gained popularity for its impressive novel view synthesis ability. This paper studies the problem of hallucinated NeRF: i.e., recovering a realistic NeRF at a different time of day from a group of tourism images. Existing solutions adopt NeRF with a controllable appearance embedding to render novel views under various conditions, but they cannot render view-consistent images with an unseen appearance. To solve this problem, we present an end-to-end framework for constructing a hallucinated NeRF, dubbed as Ha-NeRF. Specifically, we propose an appearance hallucination module to handle time-varying appearances and transfer them to novel views. Considering the complex occlusions of tourism images, we introduce an anti-occlusion module to decompose the static subjects for visibility accurately. Experimental results on synthetic data and real tourism photo collections demonstrate that our method can hallucinate the desired appearances and render occlusion-free images from different views. The project and supplementary materials are available at https://rover-xingyu.github.io/Ha-NeRF/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Hallucinated_Neural_Radiance_Fields_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Hallucinated_Neural_Radiance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15246 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Hallucinated_Neural_Radiance_Fields_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Hallucinated_Neural_Radiance_Fields_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
NeuralHDHair: Automatic High-Fidelity Hair Modeling From a Single Image Using Implicit Neural Representations | Keyu Wu, Yifan Ye, Lingchen Yang, Hongbo Fu, Kun Zhou, Youyi Zheng | Undoubtedly, high-fidelity 3D hair plays an indispensable role in digital humans. However, existing monocular hair modeling methods are either tricky to deploy in digital systems (e.g., due to their dependence on complex user interactions or large databases) or can produce only a coarse geometry. In this paper, we introduce NeuralHDHair, a flexible, fully automatic system for modeling high-fidelity hair from a single image. The key enablers of our system are two carefully designed neural networks: an IRHairNet (Implicit representation for hair using neural network) for inferring high-fidelity 3D hair geometric features (3D orientation field and 3D occupancy field) hierarchically and a GrowingNet (Growing hair strands using neural network) to efficiently generate 3D hair strands in parallel. Specifically, we perform a coarse-to-fine manner and propose a novel voxel-aligned implicit function (VIFu) to represent the global hair feature, which is further enhanced by the local details extracted from a hair luminance map. To improve the efficiency of a traditional hair growth algorithm, we adopt a local neural implicit function to grow strands based on the estimated 3D hair geometric features. Extensive experiments show that our method is capable of constructing a high-fidelity 3D hair model from a single image, both efficiently and effectively, and achieves the-state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_NeuralHDHair_Automatic_High-Fidelity_Hair_Modeling_From_a_Single_Image_Using_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_NeuralHDHair_Automatic_High-Fidelity_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.04175 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_NeuralHDHair_Automatic_High-Fidelity_Hair_Modeling_From_a_Single_Image_Using_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_NeuralHDHair_Automatic_High-Fidelity_Hair_Modeling_From_a_Single_Image_Using_CVPR_2022_paper.html | CVPR 2022 | null |
The Two Dimensions of Worst-Case Training and Their Integrated Effect for Out-of-Domain Generalization | Zeyi Huang, Haohan Wang, Dong Huang, Yong Jae Lee, Eric P. Xing | Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this "hard-to-learn" concept are mainly expanded either along the dimension of the samples or the dimension of the features. In this paper, we aim to introduce a simple view merging these two dimensions, leading to a new, simple yet effective, heuristic to train machine learning models by emphasizing the worst-cases on both the sample and the feature dimensions. We name our method W2D following the concept of "Worst-case along Two Dimensions". We validate the idea and demonstrate its empirical strength over standard benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_The_Two_Dimensions_of_Worst-Case_Training_and_Their_Integrated_Effect_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_The_Two_Dimensions_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_The_Two_Dimensions_of_Worst-Case_Training_and_Their_Integrated_Effect_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_The_Two_Dimensions_of_Worst-Case_Training_and_Their_Integrated_Effect_CVPR_2022_paper.html | CVPR 2022 | null |
Global Tracking Transformers | Xingyi Zhou, Tianwei Yin, Vladlen Koltun, Philipp Krähenbühl | We present a novel transformer-based architecture for global multi-object tracking. Our network takes a short sequence of frames as input and produces global trajectories for all objects. The core component is a global tracking transformer that operates on objects from all frames in the sequence. The transformer encodes object features from all frames, and uses trajectory queries to group them into trajectories. The trajectory queries are object features from a single frame and naturally produce unique trajectories. Our global tracking transformer does not require intermediate pairwise grouping or combinatorial association, and can be jointly trained with an object detector. It achieves competitive performance on the popular MOT17 benchmark, with 75.3 MOTA and 59.1 HOTA. More importantly, our framework seamlessly integrates into state-of-the-art large-vocabulary detectors to track any objects. Experiments on the challenging TAO dataset show that our framework consistently improves upon baselines that are based on pairwise association, outperforming published work by a significant 7.7 tracking mAP. Code is available at https://github.com/xingyizhou/GTR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Global_Tracking_Transformers_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.13250 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Global_Tracking_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Global_Tracking_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Backdoor Attacks on Self-Supervised Learning | Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Hamed Pirsiavash | Large-scale unlabeled data has spurred recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for learning representations from images (e.g., MoCo, BYOL, MSF) use an inductive bias that random augmentations (e.g., random crops) of an image should produce similar embeddings. We show that such methods are vulnerable to backdoor attacks -- where an attacker poisons a small part of the unlabeled data by adding a trigger (image patch chosen by the attacker) to the images. The model performance is good on clean test images, but the attacker can manipulate the decision of the model by showing the trigger at test time. Backdoor attacks have been studied extensively in supervised learning and to the best of our knowledge, we are the first to study them for self-supervised learning. Backdoor attacks are more practical in self-supervised learning, since the use of large unlabeled data makes data inspection to remove poisons prohibitive. We show that in our targeted attack, the attacker can produce many false positives for the target category by using the trigger at test time. We also propose a defense method based on knowledge distillation that succeeds in neutralizing the attack. Our code is available here: https://github.com/UMBCvision/SSL-Backdoor | https://openaccess.thecvf.com/content/CVPR2022/papers/Saha_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Saha_Backdoor_Attacks_on_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.10123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Saha_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Saha_Backdoor_Attacks_on_Self-Supervised_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Multimodal Token Fusion for Vision Transformers | Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, Yunhe Wang | Many adaptations of transformers have emerged to address the single-modal vision tasks, where self-attention modules are stacked to handle input sources like images. Intuitively, feeding multiple modalities of data to vision transformers could improve the performance, yet the inner-modal attentive weights may be diluted, which could thus greatly undermine the final performance. In this paper, we propose a multimodal token fusion method (TokenFusion), tailored for transformer-based vision tasks. To effectively fuse multiple modalities, TokenFusion dynamically detects uninformative tokens and substitute these tokens with projected and aggregated inter-modal features. Residual positional alignment is also adopted to enable explicit utilization of the inter-modal alignments after fusion. The design of TokenFusion allows the transformer to learn correlations among multimodal features, while the single-modal transformer architecture remains largely intact. Extensive experiments are conducted on a variety of homogeneous and heterogeneous modalities and demonstrate that TokenFusion surpasses state-of-the-art methods in three typical vision tasks: multimodal image-to-image translation, RGB-depth semantic segmentation, and 3D object detection with point cloud and images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Multimodal_Token_Fusion_for_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Multimodal_Token_Fusion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08721 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Multimodal_Token_Fusion_for_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Multimodal_Token_Fusion_for_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Frequency Adversarial Attacks for Face Forgery Detection | Shuai Jia, Chao Ma, Taiping Yao, Bangjie Yin, Shouhong Ding, Xiaokang Yang | Various facial manipulation techniques have drawn serious public concerns in morality, security, and privacy. Although existing face forgery classifiers achieve promising performance on detecting fake images, these methods are vulnerable to adversarial examples with injected imperceptible perturbations on the pixels. Meanwhile, many face forgery detectors always utilize the frequency diversity between real and fake faces as a crucial clue. In this paper, instead of injecting adversarial perturbations into the spatial domain, we propose a frequency adversarial attack method against face forgery detectors. Concretely, we apply discrete cosine transform (DCT) on the input images and introduce a fusion module to capture the salient region of adversary in the frequency domain. Compared with existing adversarial attacks (e.g. FGSM, PGD) in the spatial domain, our method is more imperceptible to human observers and does not degrade the visual quality of the original images. Moreover, inspired by the idea of meta-learning, we also propose a hybrid adversarial attack that performs attacks in both the spatial and frequency domains. Extensive experiments indicate that the proposed method fools not only the spatial-based detectors but also the state-of-the-art frequency-based detectors effectively. In addition, the proposed frequency attack enhances the transferability across face forgery detectors as black-box attacks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jia_Exploring_Frequency_Adversarial_Attacks_for_Face_Forgery_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_Exploring_Frequency_Adversarial_Attacks_for_Face_Forgery_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_Exploring_Frequency_Adversarial_Attacks_for_Face_Forgery_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
GMFlow: Learning Optical Flow via Global Matching | Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao | Learning-based optical flow estimation has been dominated with the pipeline of cost volume with convolutions for flow regression, which is inherently limited to local correlations and thus is hard to address the long-standing challenge of large displacements. To alleviate this, the state-of-the-art framework RAFT gradually improves its prediction quality by using a large number of iterative refinements, achieving remarkable performance but introducing linearly increasing inference time. To enable both high accuracy and efficiency, we completely revamp the dominant flow regression pipeline by reformulating optical flow as a global matching problem, which identifies the correspondences by directly comparing feature similarities. Specifically, we propose a GMFlow framework, which consists of three main components: a customized Transformer for feature enhancement, a correlation and softmax layer for global feature matching, and a self-attention layer for flow propagation. We further introduce a refinement step that reuses GMFlow at higher feature resolution for residual flow prediction. Our new framework outperforms 31-refinements RAFT on the challenging Sintel benchmark, while using only one refinement and running faster, suggesting a new paradigm for accurate and efficient optical flow estimation. Code is available at https://github.com/haofeixu/gmflow. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_GMFlow_Learning_Optical_Flow_via_Global_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_GMFlow_Learning_Optical_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_GMFlow_Learning_Optical_Flow_via_Global_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_GMFlow_Learning_Optical_Flow_via_Global_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation | Xian Liu, Qianyi Wu, Hang Zhou, Yinghao Xu, Rui Qian, Xinyi Lin, Xiaowei Zhou, Wayne Wu, Bo Dai, Bolei Zhou | Generating speech-consistent body and gesture movements is a long-standing problem in virtual avatar creation. Previous studies often synthesize pose movement in a holistic manner, where poses of all joints are generated simultaneously. Such a straightforward pipeline fails to generate fine-grained co-speech gestures. One observation is that the hierarchical semantics in speech and the hierarchical structures of human gestures can be naturally described into multiple granularities and associated together. To fully utilize the rich connections between speech audio and human gestures, we propose a novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech gesture generation. In HA2G, a Hierarchical Audio Learner extracts audio representations across semantic granularities. A Hierarchical Pose Inferer subsequently renders the entire human pose gradually in a hierarchical manner. To enhance the quality of synthesized gestures, we develop a contrastive learning strategy based on audio-text alignment for better audio representations. Extensive experiments and human evaluation demonstrate that the proposed method renders realistic co-speech gestures and outperforms previous methods in a clear margin. Project page: https://alvinliu0.github.io/projects/HA2G. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_Hierarchical_Cross-Modal_Association_for_Co-Speech_Gesture_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_Hierarchical_Cross-Modal_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13161 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Hierarchical_Cross-Modal_Association_for_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Hierarchical_Cross-Modal_Association_for_Co-Speech_Gesture_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
FLAVA: A Foundational Language and Vision Alignment Model | Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, Douwe Kiela | State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal (with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising direction would be to use a single holistic universal model, as a "foundation", that targets all modalities at once---a true vision and language foundation model should be good at vision tasks, language tasks, and cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate impressive performance on a wide range of 35 tasks spanning these target modalities. | https://openaccess.thecvf.com/content/CVPR2022/papers/Singh_FLAVA_A_Foundational_Language_and_Vision_Alignment_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Singh_FLAVA_A_Foundational_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04482 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_FLAVA_A_Foundational_Language_and_Vision_Alignment_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Singh_FLAVA_A_Foundational_Language_and_Vision_Alignment_Model_CVPR_2022_paper.html | CVPR 2022 | null |
Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production | Ben Saunders, Necati Cihan Camgoz, Richard Bowden | Sign languages are visual languages, with vocabularies as rich as their spoken language counterparts. However, current deep-learning based Sign Language Production (SLP) models produce under-articulated skeleton pose sequences from constrained vocabularies and this limits applicability. To be understandable and accepted by the deaf, an automatic SLP system must be able to generate co-articulated photo-realistic signing sequences for large domains of discourse. In this work, we tackle large-scale SLP by learning to co-articulate between dictionary signs, a method capable of producing smooth signing while scaling to unconstrained domains of discourse. To learn sign co-articulation, we propose a novel Frame Selection Network (FS-Net) that improves the temporal alignment of interpolated dictionary signs to continuous signing sequences. Additionally, we propose SignGAN, a pose-conditioned human synthesis model that produces photo-realistic sign language videos direct from skeleton pose. We propose a novel keypoint-based loss function which improves the quality of synthesized hand images. We evaluate our SLP model on the large-scale meineDGS (mDGS) corpus, conducting extensive user evaluation showing our FS-Net approach improves co-articulation of interpolated dictionary signs. Additionally, we show that SignGAN significantly outperforms all baseline methods for quantitative metrics, human perceptual studies and native deaf signer comprehension. | https://openaccess.thecvf.com/content/CVPR2022/papers/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Saunders_Signing_at_Scale_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15354 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Saunders_Signing_at_Scale_Learning_to_Co-Articulate_Signs_for_Large-Scale_Photo-Realistic_CVPR_2022_paper.html | CVPR 2022 | null |
Explore Spatio-Temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline | Kailai Zhou, Yibo Wang, Tao Lv, Yunqian Li, Linsen Chen, Qiu Shen, Xun Cao | We endeavor on a rarely explored task named Insubstan-tial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color. Accordingly, it is far more challenging to distinguish insubstantial objects in a single static frame and the collaborative representation of spatial and tempo-ral information is crucial. Thus, we construct an IOD-Video dataset comprised of 600 videos (141,017 frames) covering various distances, sizes, visibility, and scenes captured by different spectral ranges. In addition, we develop a spatio-temporal aggregation framework for IOD, in which differ-ent backbones are deployed and a spatio-temporal aggregation loss (STAloss) is elaborately designed to leverage the consistency along the time axis. Experiments conducted on IOD-Video dataset demonstrate that spatio-temporal aggregation can significantly improve the performance of IOD. We hope our work will attract further researches into this valuable yet challenging task. The code will be available at: https://github.com/CalayZhou/IOD-Video. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Explore_Spatio-Temporal_Aggregation_for_Insubstantial_Object_Detection_Benchmark_Dataset_and_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Explore_Spatio-Temporal_Aggregation_for_Insubstantial_Object_Detection_Benchmark_Dataset_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Explore_Spatio-Temporal_Aggregation_for_Insubstantial_Object_Detection_Benchmark_Dataset_and_CVPR_2022_paper.html | CVPR 2022 | null |
OCSampler: Compressing Videos to One Clip With Single-Step Sampling | Jintao Lin, Haodong Duan, Kai Chen, Dahua Lin, Limin Wang | Videos incorporate rich semantics as well as redundant information. Seeking a compact yet effective video representation, e.g., sample informative frames from the entire video, is critical to efficient video recognition. There have been works that formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance. In this paper, we present a more efficient framework named OCSampler, which explores such a representation with one short clip. OCSampler designs a new paradigm of learning instance-specific video condensation policies to select frames only in a single step. Rather than picking up frames sequentially like previous methods, we simply process a whole sequence at once. Accordingly, these policies are derived from a light-weighted skim network together with a simple yet effective policy network. Moreover, we extend the proposed method with a frame number budget, enabling the framework to produce correct predictions in high confidence with as few frames as possible. Experiments on various benchmarks demonstrate the effectiveness of OCSampler over previous methods in terms of accuracy and efficiency. Specifically, it achieves 76.9% mAP and 21.7 GFLOPs on ActivityNet with an impressive throughput: 123.9 Video/s on a single TITAN Xp GPU. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_OCSampler_Compressing_Videos_to_One_Clip_With_Single-Step_Sampling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_OCSampler_Compressing_Videos_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04388 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OCSampler_Compressing_Videos_to_One_Clip_With_Single-Step_Sampling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_OCSampler_Compressing_Videos_to_One_Clip_With_Single-Step_Sampling_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning | Qingsen Yan, Dong Gong, Yuhang Liu, Anton van den Hengel, Javen Qinfeng Shi | Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. Despite their performance, they still suffer from interference across tasks which leads to catastrophic forgetting. To ameliorate this problem, we propose to only activate and select sparse neurons for learning current and past tasks at any stage. More parameters space and model capacity can thus be reserved for the future tasks. This minimizes the interference between parameters for different tasks. To do so, we propose a Sparse neural Network for Continual Learning (SNCL), which employs variational Bayesian sparsity priors on the activations of the neurons in all layers. Full Experience Replay (FER) provides effective supervision in learning the sparse activations of the neurons in different layers. A loss-aware reservoir-sampling strategy is developed to maintain the memory buffer. The proposed method is agnostic as to the network structures and the task boundaries. Experiments on different datasets show that SNCL achieves state-of-the-art result for mitigating forgetting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Learning_Bayesian_Sparse_Networks_With_Full_Experience_Replay_for_Continual_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2202.10203 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Bayesian_Sparse_Networks_With_Full_Experience_Replay_for_Continual_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Learning_Bayesian_Sparse_Networks_With_Full_Experience_Replay_for_Continual_CVPR_2022_paper.html | CVPR 2022 | null |
Graph-Based Spatial Transformer With Memory Replay for Multi-Future Pedestrian Trajectory Prediction | Lihuan Li, Maurice Pagnucco, Yang Song | Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life applications such as autonomous driving and robotic motion planning. Besides generating a single future path, predicting multiple plausible future paths is becoming popular in some recent work on trajectory prediction. However, existing methods typically emphasize spatial interactions between pedestrians and surrounding areas but ignore the smoothness and temporal consistency of predictions. Our model aims to forecast multiple paths based on a historical trajectory by modeling multi-scale graph-based spatial transformers combined with a trajectory smoothing algorithm named "Memory Replay" utilizing a memory graph. Our method can comprehensively exploit the spatial information as well as correct the temporally inconsistent trajectories (e.g., sharp turns). We also propose a new evaluation metric named "Percentage of Trajectory Usage" to evaluate the comprehensiveness of diverse multi-future predictions. Our extensive experiments show that the proposed model achieves state-of-the-art performance on multi-future prediction and competitive results for single-future prediction. Code released at https://github.com/Jacobieee/ST-MR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Graph-Based_Spatial_Transformer_With_Memory_Replay_for_Multi-Future_Pedestrian_Trajectory_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Graph-Based_Spatial_Transformer_With_Memory_Replay_for_Multi-Future_Pedestrian_Trajectory_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Graph-Based_Spatial_Transformer_With_Memory_Replay_for_Multi-Future_Pedestrian_Trajectory_CVPR_2022_paper.html | CVPR 2022 | null |
Scanline Homographies for Rolling-Shutter Plane Absolute Pose | Fang Bai, Agniva Sengupta, Adrien Bartoli | Cameras on portable devices are manufactured with a rolling-shutter (RS) mechanism, where the image rows (aka. scanlines) are read out sequentially. The unknown camera motions during the imaging process cause the so-called RS effects which are solved by motion assumptions in the literature. In this work, we give a solution to the absolute pose problem free of motion assumptions. We categorically demonstrate that the only requirement is motion smoothness instead of stronger constraints on the camera motion. To this end, we propose a novel mathematical abstraction for RS cameras observing a planar scene, called the scanline-homography, a 3x2 matrix with 5 DOFs. We establish the relationship between a scanline-homography and the corresponding plane-homography, a 3x3 matrix with 6 DOFs assuming the camera is calibrated. We estimate the scanline-homographies of an RS frame using a smooth image warp powered by B-Splines, and recover the plane-homographies afterwards to obtain the scanline-poses based on motion smoothness. We back our claims with various experiments. Code and new datasets: https://bitbucket.org/clermontferrand/planarscanlinehomography/src/master/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_Scanline_Homographies_for_Rolling-Shutter_Plane_Absolute_Pose_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bai_Scanline_Homographies_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Scanline_Homographies_for_Rolling-Shutter_Plane_Absolute_Pose_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Scanline_Homographies_for_Rolling-Shutter_Plane_Absolute_Pose_CVPR_2022_paper.html | CVPR 2022 | null |
TableFormer: Table Structure Understanding With Transformers | Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar | Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a non-trivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-to-end deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. | https://openaccess.thecvf.com/content/CVPR2022/papers/Nassar_TableFormer_Table_Structure_Understanding_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nassar_TableFormer_Table_Structure_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01017 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nassar_TableFormer_Table_Structure_Understanding_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nassar_TableFormer_Table_Structure_Understanding_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Exemplar-Based Pattern Synthesis With Implicit Periodic Field Network | Haiwei Chen, Jiayi Liu, Weikai Chen, Shichen Liu, Yajie Zhao | Synthesis of ergodic, stationary visual patterns is widely applicable in texturing, shape modeling, and digital content creation. The wide applicability of this technique thus requires the pattern synthesis approaches to be scalable, diverse, and authentic. In this paper, we propose an exemplar-based visual pattern synthesis framework that aims to model the inner statistics of visual patterns and generate new, versatile patterns that meet the aforementioned requirements. To this end, we propose an implicit network based on generative adversarial network (GAN) and periodic encoding, thus calling our network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures scalability: the implicit formulation directly maps the input coordinates to features, which enables synthesis of arbitrary size and is computationally efficient for 3D shape synthesis. Learning with a periodic encoding scheme encourages diversity: the network is constrained to model the inner statistics of the exemplar based on spatial latent codes in a periodic field. Coupled with continuously designed GAN training procedures, IPFN is shown to synthesize tileable patterns with smooth transitions and local variations. Last but not least, thanks to both the adversarial training technique and the encoded Fourier features, IPFN learns high-frequency functions that produce authentic, high-quality results. To validate our approach, we present novel experimental results on various applications in 2D texture synthesis and 3D shape synthesis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.01671 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Exemplar-Based_Pattern_Synthesis_With_Implicit_Periodic_Field_Network_CVPR_2022_paper.html | CVPR 2022 | null |
Grounded Language-Image Pre-Training | Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, Jianfeng Gao | This paper presents a grounded language-image pre-training (GLIP) model for learning object-level, language-aware, and semantic-rich visual representations. GLIP unifies object detection and phrase grounding for pre-training. The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to improve both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generating grounding boxes in a self-training fashion, making the learned representations semantic-rich. In our experiments, we pre-train GLIP on 27M grounding data, including 3M human-annotated and 24M web-crawled image-text pairs. The learned representations demonstrate strong zero-shot and few-shot transferability to various object-level recognition tasks. 1) When directly evaluated on COCO and LVIS (without seeing any images in COCO during pre-training), GLIP achieves 49.8 AP and 26.9 AP, respectively, surpassing many supervised baselines. 2) After fine-tuned on COCO, GLIP achieves 60.8 AP on val and 61.5 AP on test-dev, surpassing prior SoTA. 3) When transferred to 13 downstream object detection tasks, a 1-shot GLIP rivals with a fully-supervised Dynamic Head. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Grounded_Language-Image_Pre-Training_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Grounded_Language-Image_Pre-Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03857 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Grounded_Language-Image_Pre-Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Grounded_Language-Image_Pre-Training_CVPR_2022_paper.html | CVPR 2022 | null |
Spectral Unsupervised Domain Adaptation for Visual Recognition | Jingyi Zhang, Jiaxing Huang, Zichen Tian, Shijian Lu | Though unsupervised domain adaptation (UDA) has achieved very impressive progress recently, it remains a great challenge due to missing target annotations and the rich discrepancy between source and target distributions. We propose Spectral UDA (SUDA), an effective and efficient UDA technique that works in the spectral space and can generalize across different visual recognition tasks. SUDA addresses the UDA challenges from two perspectives. First, it introduces a spectrum transformer (ST) that mitigates inter-domain discrepancies by enhancing domain-invariant spectra while suppressing domain-variant spectra of source and target samples simultaneously. Second, it introduces multi-view spectral learning that learns useful unsupervised representations by maximizing mutual information among multiple ST-generated spectral views of each target sample. Extensive experiments show that SUDA achieves superior accuracy consistently across different visual tasks in image classification, semantic segmentation, and object detection. Additionally, SUDA also works with the transformer-based network and achieves state-of-the-art performance on object detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Spectral_Unsupervised_Domain_Adaptation_for_Visual_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Spectral_Unsupervised_Domain_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.06112 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Spectral_Unsupervised_Domain_Adaptation_for_Visual_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Spectral_Unsupervised_Domain_Adaptation_for_Visual_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-Time Image Enhancement | Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, Ying Chen | The 3D Lookup Table (3D LUT) is a highly-efficient tool for real-time image enhancement tasks, which models a non-linear 3D color transform by sparsely sampling it into a discretized 3D lattice. Previous works have made efforts to learn image-adaptive output color values of LUTs for flexible enhancement but neglect the importance of sampling strategy. They adopt a sub-optimal uniform sampling point allocation, limiting the expressiveness of the learned LUTs since the (tri-)linear interpolation between uniform sampling points in the LUT transform might fail to model local non-linearities of the color transform. Focusing on this problem, we present AdaInt (Adaptive Intervals Learning), a novel mechanism to achieve a more flexible sampling point allocation by adaptively learning the non-uniform sampling intervals in the 3D color space. In this way, a 3D LUT can increase its capability by conducting dense sampling in color ranges requiring highly non-linear transforms and sparse sampling for near-linear transforms. The proposed AdaInt could be implemented as a compact and efficient plug-and-play module for a 3D LUT-based method. To enable the end-to-end learning of AdaInt, we design a novel differentiable operator called AiLUT-Transform (Adaptive Interval LUT Transform) to locate input colors in the non-uniform 3D LUT and provide gradients to the sampling intervals. Experiments demonstrate that methods equipped with AdaInt can achieve state-of-the-art performance on two public benchmark datasets with a negligible overhead increase. Our source code is available at https://github.com/ImCharlesY/AdaInt. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_AdaInt_Learning_Adaptive_Intervals_for_3D_Lookup_Tables_on_Real-Time_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_AdaInt_Learning_Adaptive_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13983 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_AdaInt_Learning_Adaptive_Intervals_for_3D_Lookup_Tables_on_Real-Time_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_AdaInt_Learning_Adaptive_Intervals_for_3D_Lookup_Tables_on_Real-Time_CVPR_2022_paper.html | CVPR 2022 | null |
PatchFormer: An Efficient Point Transformer With Patch Attention | Cheng Zhang, Haocheng Wan, Xinyi Shen, Zizhao Wu | The point cloud learning community is witnesses a modeling shift from CNNs to Transformers, where pure Transformer architectures have achieved top accuracy on the major learning benchmarks. However, existing point Transformers are computationally expensive since they need to generate a large attention map, which has quadratic complexity (both in space and time) with respect to input size. To solve this shortcoming, we introduce patch-attention (PAT) to adaptively learn a much smaller set of bases upon which the attention maps are computed. By a weighted summation upon these bases, PAT not only captures the global shape context but also achieves linear complexity to input size. In addition, we propose a lightweight Multi-Scale Attention (MST) block to build attentions among features of different scales, providing the model with multi-scale features. Equipped with the PAT and MST, we construct our neural architecture called PatchFormer that integrates both modules into a joint framework for point cloud learning. Extensive experiments demonstrate that our network achieves comparable accuracy on general point cloud learning tasks with 9.2x speed-up than previous point Transformers. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_PatchFormer_An_Efficient_Point_Transformer_With_Patch_Attention_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.00207 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_PatchFormer_An_Efficient_Point_Transformer_With_Patch_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_PatchFormer_An_Efficient_Point_Transformer_With_Patch_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
Recurrent Glimpse-Based Decoder for Detection With Transformer | Zhe Chen, Jing Zhang, Dacheng Tao | Although detection with Transformer (DETR) is increasingly popular, its global attention modeling requires an extremely long training period to optimize and achieve promising detection performance. Alternative to existing studies that mainly develop advanced feature or embedding designs to tackle the training issue, we point out that the Region-of-Interest (RoI) based detection refinement can easily help mitigate the difficulty of training for DETR methods. Based on this, we introduce a novel REcurrent Glimpse-based decOder (REGO) in this paper. In particular, the REGO employs a multi-stage recurrent processing structure to help the attention of DETR gradually focus on foreground objects more accurately. In each processing stage, visual features are extracted as glimpse features from RoIs with enlarged bounding box areas of detection results from the previous stage. Then, a glimpse-based decoder is introduced to provide refined detection results based on both the glimpse features and the attention modeling outputs of the previous stage. In practice, REGO can be easily embedded in representative DETR variants while maintaining their fully end-to-end training and inference pipelines. In particular, REGO helps Deformable DETR achieve 44.8 AP on the MSCOCO dataset with only 36 training epochs, compared with the first DETR and the Deformable DETR that require 500 and 50 epochs to achieve comparable performance, respectively. Experiments also show that REGO consistently boosts the performance of different DETR detectors by up to 7% relative gain at the same setting of 50 training epochs. Code is available via https://github.com/zhechen/Deformable-DETR-REGO. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Recurrent_Glimpse-Based_Decoder_for_Detection_With_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Recurrent_Glimpse-Based_Decoder_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04632 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Recurrent_Glimpse-Based_Decoder_for_Detection_With_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Recurrent_Glimpse-Based_Decoder_for_Detection_With_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction To Treat Diabetic Foot Ulcers | Han Joo Chae, Seunghwan Lee, Hyewon Son, Seungyeob Han, Taebin Lim | We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surgery to treat diabetic foot ulcers (DFUs). AiD Regen seamlessly binds the full pipeline, which includes RGB-D image capturing, semantic segmentation, boundary-guided point-cloud processing, 3D model reconstruction, and 3D printable G-code generation, into a single system that can be used out of the box. We developed a multi-stage data preprocessing method to handle small and unbalanced DFU image datasets. AiD Regen's human-in-the-loop machine learning interface enables clinicians to not only create 3D regenerative patches with just a few touch interactions but also customize and confirm wound boundaries. As evidenced by our experiments, our model outperforms prior wound segmentation models and our reconstruction algorithm is capable of generating 3D wound models with compelling accuracy. We further conducted a case study on a real DFU patient and demonstrated the effectiveness of AiD Regen in treating DFU wounds. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chae_Generating_3D_Bio-Printable_Patches_Using_Wound_Segmentation_and_Reconstruction_To_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chae_Generating_3D_Bio-Printable_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.03814 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chae_Generating_3D_Bio-Printable_Patches_Using_Wound_Segmentation_and_Reconstruction_To_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chae_Generating_3D_Bio-Printable_Patches_Using_Wound_Segmentation_and_Reconstruction_To_CVPR_2022_paper.html | CVPR 2022 | null |
SimMIM: A Simple Framework for Masked Image Modeling | Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, Han Hu | This paper presents SimMIM, a simple framework for masked image modeling. We have simplified recently proposed relevant approaches, without the need for special designs, such as block-wise masking and tokenization via discrete VAE or clustering. To investigate what makes a masked image modeling task learn good representations, we systematically study the major components in our framework, and find that the simple designs of each component have revealed very strong representation learning performance: 1) random masking of the input image with a moderately large masked patch size (e.g., 32) makes a powerful pre-text task; 2) predicting RGB values of raw pixels by direct regression performs no worse than the patch classification approaches with complex designs; 3) the prediction head can be as light as a linear layer, with no worse performance than heavier ones. Using ViT-B, our approach achieves 83.8% top-1 fine-tuning accuracy on ImageNet-1K by pre-training also on this dataset, surpassing previous best approach by +0.6%. When applied to a larger model with about 650 million parameters, SwinV2-H, it achieves 87.1% top-1 accuracy on ImageNet-1K using only ImageNet-1K data. We also leverage this approach to address the data-hungry issue faced by large-scale model training, that a 3B model (SwinV2-G) is successfully trained to achieve state-of-the-art accuracy on four representative vision benchmarks using 40x less labeled data than that in previous practice (JFT-3B). The code is available at https://github.com/microsoft/SimMIM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_SimMIM_A_Simple_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.09886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_SimMIM_A_Simple_Framework_for_Masked_Image_Modeling_CVPR_2022_paper.html | CVPR 2022 | null |
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion | Yuyan Li, Yuliang Guo, Zhixin Yan, Xinyu Huang, Ye Duan, Liu Ren | A well-known challenge in applying deep-learning methods to omnidirectional images is spherical distortion. In dense regression tasks such as depth estimation, where structural details are required, using a vanilla CNN layer on the distorted 360 image results in undesired information loss. In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue. Our pipeline transforms a 360 image into less-distorted perspective patches (i.e. tangent images) to obtain patch-wise predictions via CNN, and then merge the patch-wise results for final output. To handle the discrepancy between patch-wise predictions which is a major issue affecting the merging quality, we propose a new framework with the following key components. First, we propose a geometry-aware feature fusion mechanism that combines 3D geometric features with 2D image features to compensate for the patch-wise discrepancy. Second, we employ the self-attention-based transformer architecture to conduct a global aggregation of patch-wise information, which further improves the consistency. Last, we introduce an iterative depth refinement mechanism, to further refine the estimated depth based on the more accurate geometric features. Experiments show that our method greatly mitigates the distortion issue, and achieves state-of-the-art performances on several 360 monocular depth estimation benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_OmniFusion_360_Monocular_Depth_Estimation_via_Geometry-Aware_Fusion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_OmniFusion_360_Monocular_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.00838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_OmniFusion_360_Monocular_Depth_Estimation_via_Geometry-Aware_Fusion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_OmniFusion_360_Monocular_Depth_Estimation_via_Geometry-Aware_Fusion_CVPR_2022_paper.html | CVPR 2022 | null |
Label Matching Semi-Supervised Object Detection | Binbin Chen, Weijie Chen, Shicai Yang, Yunyi Xuan, Jie Song, Di Xie, Shiliang Pu, Mingli Song, Yueting Zhuang | Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training. Despite the promising results, the label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training. In this paper, we delve into this problem and propose a simple yet effective LabelMatch framework from two different yet complementary perspectives, i.e., distribution-level and instance-level. For the former one, it is reasonable to approximate the class distribution of the unlabeled data from that of the labeled data according to Monte Carlo Sampling. Guided by this weakly supervision cue, we introduce a re-distribution mean teacher, which leverages adaptive label-distribution-aware confidence thresholds to generate unbiased pseudo labels to drive student learning. For the latter one, there exists an overlooked label assignment ambiguity problem across teacher-student models. To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly. Experiments on both MS-COCO and PASCAL-VOC datasets demonstrate the considerable superiority of our proposed framework to other state-of-the-arts. Code will be available at https://github.com/HIK-LAB/SSOD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Label_Matching_Semi-Supervised_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
RegionCLIP: Region-Based Language-Image Pretraining | Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, Jianfeng Gao | Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to unsatisfactory performance due to a major domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans. To mitigate this issue, we propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts. Our method leverages a CLIP model to match image regions with template captions, and then pretrains our model to align these region-text pairs in the feature space. When transferring our pretrained model to the open-vocabulary object detection task, our method outperforms the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respectively. Further, the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets. Our code is available at https://github.com/microsoft/RegionCLIP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_RegionCLIP_Region-Based_Language-Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09106 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.html | CVPR 2022 | null |
Video Frame Interpolation Transformer | Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, Ming-Hsuan Yang | Existing methods for video interpolation heavily rely on deep convolution neural networks, and thus suffer from their intrinsic limitations, such as content-agnostic kernel weights and restricted receptive field. To address these issues, we propose a Transformer-based video interpolation framework that allows content-aware aggregation weights and considers long-range dependencies with the self-attention operations. To avoid the high computational cost of global self-attention, we introduce the concept of local attention into video interpolation and extend it to the spatial-temporal domain. Furthermore, we propose a space-time separation strategy to save memory usage, which also improves performance. In addition, we develop a multi-scale frame synthesis scheme to fully realize the potential of Transformers. Extensive experiments demonstrate the proposed model performs favorably against the state-of-the-art methods both quantitatively and qualitatively on a variety of benchmark datasets. The code and models are released at https://github.com/zhshi0816/ Video-Frame-Interpolation-Transformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_Video_Frame_Interpolation_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.13817 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Video_Frame_Interpolation_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Video_Frame_Interpolation_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation | Cheng-Kun Yang, Ji-Jia Wu, Kai-Syun Chen, Yung-Yu Chuang, Yen-Yu Lin | We address weakly supervised point cloud segmentation by proposing a new model, MIL-derived transformer, to mine additional supervisory signals. First, the transformer model is derived based on multiple instance learning (MIL) to explore pair-wise cloud-level supervision, where two clouds of the same category yield a positive bag while two of different classes produce a negative bag. It leverages not only individual cloud annotations but also pair-wise cloud semantics for model optimization. Second, Adaptive global weighted pooling (AdaGWP) is integrated into our transformer model to replace max pooling and average pooling. It introduces learnable weights to re-scale logits in the class activation maps. It is more robust to noise while discovering more complete foreground points under weak supervision. Third, we perform point subsampling and enforce feature equivariance between the original and subsampled point clouds for regularization. The proposed method is end-to-end trainable and is general because it can work with different backbones with diverse types of weak supervision signals, including sparsely annotated points and cloud-level labels. The experiments show that it achieves state-of-the-art performance on the S3DIS and ScanNet benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_An_MIL-Derived_Transformer_for_Weakly_Supervised_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_An_MIL-Derived_Transformer_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_An_MIL-Derived_Transformer_for_Weakly_Supervised_Point_Cloud_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_An_MIL-Derived_Transformer_for_Weakly_Supervised_Point_Cloud_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Fast Light-Weight Near-Field Photometric Stereo | Daniel Lichy, Soumyadip Sengupta, David W. Jacobs | We introduce the first end-to-end learning-based solution to near-field Photometric Stereo (PS), where the light sources are close to the object of interest. This setup is especially useful for reconstructing large immobile objects. Our method is fast, producing a mesh from 52 512x384 resolution images in about 1 second on a commodity GPU, thus potentially unlocking several AR/VR applications. Existing approaches rely on optimization coupled with a far-field PS network operating on pixels or small patches. Using optimization makes these approaches slow and memory intensive (requiring 17GB GPU and 27GB of CPU memory) while using only pixels or patches makes them highly susceptible to noise and calibration errors. To address these issues, we develop a recursive multi-resolution scheme to estimate surface normal and depth maps of the whole image at each step. The predicted depth map at each scale is then used to estimate 'per-pixel lighting' for the next scale. This design makes our approach almost 45x faster and 2 degrees more accurate (11.3 vs. 13.3 degrees Mean Angular Error) than the state-of-the-art near-field PS reconstruction technique, which uses iterative optimization. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lichy_Fast_Light-Weight_Near-Field_Photometric_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lichy_Fast_Light-Weight_Near-Field_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lichy_Fast_Light-Weight_Near-Field_Photometric_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lichy_Fast_Light-Weight_Near-Field_Photometric_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
BCOT: A Markerless High-Precision 3D Object Tracking Benchmark | Jiachen Li, Bin Wang, Shiqiang Zhu, Xin Cao, Fan Zhong, Wenxuan Chen, Te Li, Jason Gu, Xueying Qin | Template-based 3D object tracking still lacks a high-precision benchmark of real scenes due to the difficulty of annotating the accurate 3D poses of real moving video objects without using markers. In this paper, we present a multi-view approach to estimate the accurate 3D poses of real moving objects, and then use binocular data to construct a new benchmark for monocular textureless 3D object tracking. The proposed method requires no markers, and the cameras only need to be synchronous, relatively fixed as cross-view and calibrated. Based on our object-centered model, we jointly optimize the object pose by minimizing shape re-projection constraints in all views, which greatly improves the accuracy compared with the single-view approach, and is even more accurate than the depth-based method. Our new benchmark dataset contains 20 textureless objects, 22 scenes, 404 video sequences and 126K images captured in real scenes. The annotation error is guaranteed to be less than 2mm, according to both theoretical analysis and validation experiments. We re-evaluate the state-of-the-art 3D object tracking methods with our dataset, reporting their performance ranking in real scenes. Our BCOT benchmark and code can be found at https://ar3dv.github.io/BCOT-Benchmark/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_BCOT_A_Markerless_High-Precision_3D_Object_Tracking_Benchmark_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_BCOT_A_Markerless_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_BCOT_A_Markerless_High-Precision_3D_Object_Tracking_Benchmark_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_BCOT_A_Markerless_High-Precision_3D_Object_Tracking_Benchmark_CVPR_2022_paper.html | CVPR 2022 | null |
Omni-DETR: Omni-Supervised Object Detection With Transformers | Pei Wang, Zhaowei Cai, Hao Yang, Gurumurthy Swaminathan, Nuno Vasconcelos, Bernt Schiele, Stefano Soatto | We consider the problem of omni-supervised object detection, which can use unlabeled, fully labeled and weakly labeled annotations, such as image tags, counts, points, etc., for object detection. This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection. Under this unified architecture, different types of weak labels can be leveraged to generate accurate pseudo labels, by a bipartite matching based filtering mechanism, for the model to learn. In the experiments, Omni-DETR has achieved state-of-the-art results on multiple datasets and settings. And we have found that weak annotations can help to improve detection performance and a mixture of them can achieve a better trade-off between annotation cost and accuracy than the standard complete annotation. These findings could encourage larger object detection datasets with mixture annotations. The code is available at https://github.com/amazon-research/omni-detr. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Omni-DETR_Omni-Supervised_Object_Detection_With_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Omni-DETR_Omni-Supervised_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Omni-DETR_Omni-Supervised_Object_Detection_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Omni-DETR_Omni-Supervised_Object_Detection_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Uniform Subdivision of Omnidirectional Camera Space for Efficient Spherical Stereo Matching | Donghun Kang, Hyeonjoong Jang, Jungeon Lee, Chong-Min Kyung, Min H. Kim | Omnidirectional cameras have been used widely to better understand surrounding environments. They are often configured as stereo to estimate depth. However, due to the optics of the fisheye lens, conventional epipolar geometry is inapplicable directly to omnidirectional camera images. Intermediate formats of omnidirectional images, such as equirectangular images, have been used. However, stereo matching performance on these image formats has been lower than the conventional stereo due to severe image distortion near pole regions. In this paper, to address the distortion problem of omnidirectional images, we devise a novel subdivision scheme of a spherical geodesic grid. This enables more isotropic patch sampling of spherical image information in the omnidirectional camera space. Our spherical geodesic grid is tessellated with an equal-arc subdivision, making the cell sizes and in-between distances as uniform as possible, i.e., the arc length of the spherical grid cell's edges is well regularized. Also, our uniformly tessellated coordinates in a 2D image can be transformed into spherical coordinates via one-to-one mapping, allowing for analytical forward/backward transformation. Our uniform tessellation scheme achieves a higher accuracy of stereo matching than the traditional cylindrical and cubemap-based approaches, reducing the memory footage required for stereo matching by 20 %. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kang_Uniform_Subdivision_of_Omnidirectional_Camera_Space_for_Efficient_Spherical_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kang_Uniform_Subdivision_of_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_Uniform_Subdivision_of_Omnidirectional_Camera_Space_for_Efficient_Spherical_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_Uniform_Subdivision_of_Omnidirectional_Camera_Space_for_Efficient_Spherical_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
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