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IntentVizor: Towards Generic Query Guided Interactive Video Summarization | Guande Wu, Jianzhe Lin, Claudio T. Silva | The target of automatic video summarization is to create a short skim of the original long video while preserving the major content/events. There is a growing interest in the integration of user queries into video summarization or query-driven video summarization. This video summarization method predicts a concise synopsis of the original video based on the user query, which is commonly represented by the input text. However, two inherent problems exist in this query-driven way. First, the text query might not be enough to describe the exact and diverse needs of the user. Second, the user cannot edit once the summaries are produced, while we assume the needs of the user should be subtle and need to be adjusted interactively. To solve these two problems, we propose IntentVizor, an interactive video summarization framework guided by generic multi-modality queries. The input query that describes the user's needs are not limited to text but also the video snippets. We further represent these multi-modality finer-grained queries as user 'intent', which is interpretable, interactable, editable, and can better quantify the user's needs. In this paper, we use a set of the proposed intents to represent the user query and design a new interactive visual analytic interface. Users can interactively control and adjust these mixed-initiative intents to obtain a more satisfying summary through the interface. Also, to improve the summarization quality via video understanding, a novel Granularity-Scalable Ego-Graph Convolutional Networks (GSE-GCN) is proposed. We conduct our experiments on two benchmark datasets. Comparisons with the state-of-the-art methods verify the effectiveness of the proposed framework. Code and dataset are available at https://github.com/jnzs1836/intent-vizor. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_IntentVizor_Towards_Generic_Query_Guided_Interactive_Video_Summarization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_IntentVizor_Towards_Generic_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2109.14834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_IntentVizor_Towards_Generic_Query_Guided_Interactive_Video_Summarization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_IntentVizor_Towards_Generic_Query_Guided_Interactive_Video_Summarization_CVPR_2022_paper.html | CVPR 2022 | null |
Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks | Wenwen Pan, Haonan Shi, Zhou Zhao, Jieming Zhu, Xiuqiang He, Zhigeng Pan, Lianli Gao, Jun Yu, Fei Wu, Qi Tian | Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions. However, the existing referring video semantic segmentation works mainly focus on the guidance of text-based referring expressions, due to the lack of modeling the semantic representation of audio-video interaction contents. In this paper, we consider the problem of audio-guided video semantic segmentation from the viewpoint of end-to-end denoised encoder-decoder network learning. We propose the walvelet-based encoder network to learn the crossmodal representations of the video contents with audio-form queries. Specifically, we adopt a multi-head cross-modal attention to explore the potential relations of video and query contents. A 2-dimension discrete wavelet transform is employed to decompose the audio-video features. We quantify the thresholds of high frequency coefficients to filter the noise and outliers. Then, a self attention-free decoder network is developed to generate the target masks with frequency domain transforms. Moreover, we maximize mutual information between the encoded features and multi-modal features after cross-modal attention to enhance the audio guidance. In addition, we construct the first large-scale audio-guided video semantic segmentation dataset. The extensive experiments show the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pan_Wnet_Audio-Guided_Video_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Wnet_Audio-Guided_Video_Object_Segmentation_via_Wavelet-Based_Cross-Modal_Denoising_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Camera Pose Estimation Using Implicit Distortion Models | Linfei Pan, Marc Pollefeys, Viktor Larsson | Low-dimensional parametric models are the de-facto standard in computer vision for intrinsic camera calibration. These models explicitly describe the mapping between incoming viewing rays and image pixels. In this paper, we explore an alternative approach which implicitly models the lens distortion. The main idea is to replace the parametric model with a regularization term that ensures the latent distortion map varies smoothly throughout the image. The proposed model is effectively parameter-free and allows us to optimize the 6 degree-of-freedom camera pose without explicitly knowing the intrinsic calibration. We show that the method is applicable to a wide selection of cameras with varying distortion and in multiple applications, such as visual localization and structure-from-motion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pan_Camera_Pose_Estimation_Using_Implicit_Distortion_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pan_Camera_Pose_Estimation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Camera_Pose_Estimation_Using_Implicit_Distortion_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_Camera_Pose_Estimation_Using_Implicit_Distortion_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations | Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lučić, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, Andrea Tagliasacchi | A classical problem in computer vision is to infer a 3D scene representation from few images that can be used to render novel views at interactive rates. Previous work focuses on reconstructing pre-defined 3D representations, e.g. textured meshes, or implicit representations, e.g. radiance fields, and often requires input images with precise camera poses and long processing times for each novel scene. In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass. To calculate the scene representation, we propose a generalization of the Vision Transformer to sets of images, enabling global information integration, and hence 3D reasoning. An efficient decoder transformer parameterizes the light field by attending into the scene representation to render novel views. Learning is supervised end-to-end by minimizing a novel-view reconstruction error. We show that this method outperforms recent baselines in terms of PSNR and speed on synthetic datasets, including a new dataset created for the paper. Further, we demonstrate that SRT scales to support interactive visualization and semantic segmentation of real-world outdoor environments using Street View imagery. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sajjadi_Scene_Representation_Transformer_Geometry-Free_Novel_View_Synthesis_Through_Set-Latent_Scene_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sajjadi_Scene_Representation_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sajjadi_Scene_Representation_Transformer_Geometry-Free_Novel_View_Synthesis_Through_Set-Latent_Scene_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sajjadi_Scene_Representation_Transformer_Geometry-Free_Novel_View_Synthesis_Through_Set-Latent_Scene_CVPR_2022_paper.html | CVPR 2022 | null |
Shape-Invariant 3D Adversarial Point Clouds | Qidong Huang, Xiaoyi Dong, Dongdong Chen, Hang Zhou, Weiming Zhang, Nenghai Yu | Adversary and invisibility are two fundamental but conflict characters of adversarial perturbations. Previous adversarial attacks on 3D point cloud recognition have often been criticized for their noticeable point outliers, since they just involve an "implicit constrain" like global distance loss in the time-consuming optimization to limit the generated noise. While point cloud is a highly structured data format, it is hard to constrain its perturbation with a simple loss or metric properly. In this paper, we propose a novel Point-Cloud Sensitivity Map to boost both the efficiency and imperceptibility of point perturbations. This map reveals the vulnerability of point cloud recognition models when encountering shape-invariant adversarial noises. These noises are designed along the shape surface with an "explicit constrain" instead of extra distance loss. Specifically, we first apply a reversible coordinate transformation on each point of the point cloud input, to reduce one degree of point freedom and limit its movement on the tangent plane. Then we calculate the best attacking direction with the gradients of the transformed point cloud obtained on the white-box model. Finally we assign each point with a non-negative score to construct the sensitivity map, which benefits both white-box adversarial invisibility and black-box query-efficiency extended in our work. Extensive evaluations prove that our method can achieve the superior performance on various point cloud recognition models, with its satisfying adversarial imperceptibility and strong resistance to different point cloud defense settings. Our code is available at: https://github.com/shikiw/SI-Adv. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Shape-Invariant_3D_Adversarial_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Shape-Invariant_3D_Adversarial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04041 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Shape-Invariant_3D_Adversarial_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Shape-Invariant_3D_Adversarial_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
LAS-AT: Adversarial Training With Learnable Attack Strategy | Xiaojun Jia, Yong Zhang, Baoyuan Wu, Ke Ma, Jue Wang, Xiaochun Cao | Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial examples (AEs). Most previous methods adopt Projected Gradient Decent (PGD) with manually specifying attack parameters for AE generation. A combination of the attack parameters can be referred to as an attack strategy. Several works have revealed that using a fixed attack strategy to generate AEs during the whole training phase limits the model robustness and propose to exploit different attack strategies at different training stages to improve robustness. But those multi-stage hand-crafted attack strategies need much domain expertise, and the robustness improvement is limited. In this paper, we propose a novel framework for adversarial training by introducing the concept of "learnable attack strategy", dubbed LAS-AT, which learns to automatically produce attack strategies to improve the model robustness. Our framework is composed of a target network that uses AEs for training to improve robustness, and a strategy network that produces attack strategies to control the AE generation. Experimental evaluations on three benchmark databases demonstrate the superiority of the proposed method, and the proposed method outperforms state-of-the-art adversarial training methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jia_LAS-AT_Adversarial_Training_With_Learnable_Attack_Strategy_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jia_LAS-AT_Adversarial_Training_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_LAS-AT_Adversarial_Training_With_Learnable_Attack_Strategy_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jia_LAS-AT_Adversarial_Training_With_Learnable_Attack_Strategy_CVPR_2022_paper.html | CVPR 2022 | null |
Bootstrapping ViTs: Towards Liberating Vision Transformers From Pre-Training | Haofei Zhang, Jiarui Duan, Mengqi Xue, Jie Song, Li Sun, Mingli Song | Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV). With the general-purpose Transformer architecture replacing the hard-coded inductive biases of convolution, ViTs have surpassed CNNs, especially in data-sufficient circumstances. However, ViTs are prone to over-fit on small datasets and thus rely on large-scale pre-training, which expends enormous time. In this paper, we strive to liberate ViTs from pre-training by introducing CNNs' inductive biases back to ViTs while preserving their network architectures for higher upper bound and setting up more suitable optimization objectives. To begin with, an agent CNN is designed based on the given ViT with inductive biases. Then a bootstrapping training algorithm is proposed to jointly optimize the agent and ViT with weight sharing, during which the ViT learns inductive biases from the intermediate features of the agent. Extensive experiments on CIFAR-10/100 and ImageNet-1k with limited training data have shown encouraging results that the inductive biases help ViTs converge significantly faster and outperform conventional CNNs with even fewer parameters. Our code is publicly available at https://github.com/zhfeing/Bootstrapping-ViTs-pytorch. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Bootstrapping_ViTs_Towards_Liberating_Vision_Transformers_From_Pre-Training_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Bootstrapping_ViTs_Towards_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.03552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Bootstrapping_ViTs_Towards_Liberating_Vision_Transformers_From_Pre-Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Bootstrapping_ViTs_Towards_Liberating_Vision_Transformers_From_Pre-Training_CVPR_2022_paper.html | CVPR 2022 | null |
PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents | Brandon Smock, Rohith Pesala, Robin Abraham | Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks. Data and code will be released at https://github.com/microsoft/table-transformer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Smock_PubTables-1M_Towards_Comprehensive_Table_Extraction_From_Unstructured_Documents_CVPR_2022_paper.html | CVPR 2022 | null |
Styleformer: Transformer Based Generative Adversarial Networks With Style Vector | Jeeseung Park, Younggeun Kim | We propose Styleformer, a generator that synthesizes image using style vectors based on the Transformer structure. In this paper, we effectively apply the modified Transformer structure (e.g., Increased multi-head attention and Pre-layer normalization) and attention style injection which is style modulation and demodulation method for self-attention operation. The new generator components have strengths in CNN's shortcomings, handling long-range dependency and understanding global structure of objects. We propose two methods to generate high-resolution images using Styleformer. First, we apply Linformer in the field of visual synthesis (Styleformer-L), enabling Styleformer to generate higher resolution images and result in improvements in terms of computation cost and performance. This is the first case using Linformer to image generation. Second, we combine Styleformer and StyleGAN2 (Styleformer-C) to generate high-resolution compositional scene efficiently, which Styleformer captures long-range-dependencies between components. With these adaptations, Styleformer achieves comparable performances to state-of-the-art in both single and multi-object datasets. Furthermore, groundbreaking results from style mixing and attention map visualization demonstrate the advantages and efficiency of our model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Styleformer_Transformer_Based_Generative_Adversarial_Networks_With_Style_Vector_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Styleformer_Transformer_Based_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.07023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Styleformer_Transformer_Based_Generative_Adversarial_Networks_With_Style_Vector_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Styleformer_Transformer_Based_Generative_Adversarial_Networks_With_Style_Vector_CVPR_2022_paper.html | CVPR 2022 | null |
Efficient Two-Stage Detection of Human-Object Interactions With a Novel Unary-Pairwise Transformer | Frederic Z. Zhang, Dylan Campbell, Stephen Gould | Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks. In particular, using learnable queries in place of region proposals has given rise to a new class of one-stage detection models, spearheaded by the Detection Transformer (DETR). Variations on this one-stage approach have since dominated human-object interaction (HOI) detection. However, the success of such one-stage HOI detectors can largely be attributed to the representation power of transformers. We discovered that when equipped with the same transformer, their two-stage counterparts can be more performant and memory-efficient, while taking a fraction of the time to train. In this work, we propose the Unary-Pairwise Transformer, a two-stage detector that exploits unary and pairwise representations for HOIs. We observe that the unary and pairwise parts of our transformer network specialise, with the former preferentially increasing the scores of positive examples and the latter decreasing the scores of negative examples. We evaluate our method on the HICO-DET and V-COCO datasets, and significantly outperform state-of-the-art approaches. At inference time, our model with ResNet50 approaches real-time performance on a single GPU. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Efficient_Two-Stage_Detection_of_Human-Object_Interactions_With_a_Novel_Unary-Pairwise_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Efficient_Two-Stage_Detection_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01838 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Efficient_Two-Stage_Detection_of_Human-Object_Interactions_With_a_Novel_Unary-Pairwise_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Efficient_Two-Stage_Detection_of_Human-Object_Interactions_With_a_Novel_Unary-Pairwise_CVPR_2022_paper.html | CVPR 2022 | null |
ELSR: Efficient Line Segment Reconstruction With Planes and Points Guidance | Dong Wei, Yi Wan, Yongjun Zhang, Xinyi Liu, Bin Zhang, Xiqi Wang | Three-dimensional (3D) line segments are helpful for scene reconstruction. Most of the existing 3D-line-segment-reconstruction algorithms deal with two views or dozens of small-size images; while in practice there are usually hundreds or thousands of large-size images. In this paper, we propose an efficient line segment reconstruction method called ELSR. ELSR exploits scene planes that are commonly seen in city scenes and sparse 3D points that can be acquired easily from the structure-from-motion (SfM) approach. For two views, ELSR efficiently finds the local scene plane to guide the line matching and exploits sparse 3D points to accelerate and constrain the matching. To reconstruct a 3D line segment with multiple views, ELSR utilizes an efficient abstraction approach that selects representative 3D lines based on their spatial consistence. Our experiments demonstrated that ELSR had a higher accuracy and efficiency than the existing methods. Moreover, our results showed that ELSR could reconstruct 3D lines efficiently for large and complex scenes that contain thousands of large-size images. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_ELSR_Efficient_Line_Segment_Reconstruction_With_Planes_and_Points_Guidance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_ELSR_Efficient_Line_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_ELSR_Efficient_Line_Segment_Reconstruction_With_Planes_and_Points_Guidance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_ELSR_Efficient_Line_Segment_Reconstruction_With_Planes_and_Points_Guidance_CVPR_2022_paper.html | CVPR 2022 | null |
Meta-Attention for ViT-Backed Continual Learning | Mengqi Xue, Haofei Zhang, Jie Song, Mingli Song | Continual learning is a longstanding research topic due to its crucial role in tackling continually arriving tasks. Up to now, the study of continual learning in computer vision is mainly restricted to convolutional neural networks (CNNs). However, recently there is a tendency that the newly emerging vision transformers (ViTs) are gradually dominating the field of computer vision, which leaves CNN-based continual learning lagging behind as they can suffer from severe performance degradation if straightforwardly applied to ViTs. In this paper, we study ViT-backed continual learning to strive for higher performance riding on recent advances of ViTs. Inspired by mask-based continual learning methods in CNNs, where a mask is learned per task to adapt the pre-trained ViT to the new task, we propose MEta-ATtention (MEAT), i.e., attention to self-attention, to adapt a pre-trained ViT to new tasks without sacrificing performance on already learned tasks. Unlike prior mask-based methods like Piggyback, where all parameters are associated with corresponding masks, MEAT leverages the characteristics of ViTs and only masks a portion of its parameters. It renders MEAT more efficient and effective with less overhead and higher accuracy. Extensive experiments demonstrate that MEAT exhibits significant superiority to its state-of-the-art CNN counterparts, with 4.0 6.0% absolute boosts in accuracy. Our code has been released at https://github.com/zju-vipa/MEAT-TIL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Meta-Attention_for_ViT-Backed_Continual_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xue_Meta-Attention_for_ViT-Backed_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11684 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Meta-Attention_for_ViT-Backed_Continual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Meta-Attention_for_ViT-Backed_Continual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
DST: Dynamic Substitute Training for Data-Free Black-Box Attack | Wenxuan Wang, Xuelin Qian, Yanwei Fu, Xiangyang Xue | With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples. For data-free black box attack scenario, existing methods are inspired by the knowledge distillation, and thus usually train a substitute model to learn knowledge from the target model using generated data as input. However, the substitute model always has a static network structure, which limits the attack ability for various target models and tasks. In this paper, we propose a novel dynamic substitute training attack method to encourage substitute model to learn better and faster from the target model. Specifically, a dynamic substitute structure learning strategy is proposed to adaptively generate optimal substitute model structure via a dynamic gate according to different target models and tasks. Moreover, we introduce a task-driven graph-based structure information learning constrain to improve the quality of generated training data, and facilitate the substitute model learning structural relationships from the target model multiple outputs. Extensive experiments have been conducted to verify the efficacy of the proposed attack method, which can achieve better performance compared with the state-of-the-art competitors on several datasets. Project page: https://wxwangiris.github.io/DST | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_DST_Dynamic_Substitute_Training_for_Data-Free_Black-Box_Attack_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.00972 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_DST_Dynamic_Substitute_Training_for_Data-Free_Black-Box_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_DST_Dynamic_Substitute_Training_for_Data-Free_Black-Box_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing | Thiemo Alldieck, Mihai Zanfir, Cristian Sminchisescu | We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image. Our pixel-aligned method estimates detailed 3D geometry and, for the first time, the unshaded surface color together with the scene illumination. Observing that 3D supervision alone is not sufficient for high fidelity color reconstruction, we introduce patch-based rendering losses that enable reliable color reconstruction on visible parts of the human, and detailed and plausible color estimation for the non-visible parts. Moreover, our method specifically addresses methodological and practical limitations of prior work in terms of representing geometry, albedo, and illumination effects, in an end-to-end model where factors can be effectively disentangled. In extensive experiments, we demonstrate the versatility and robustness of our approach. Our state-of-the-art results validate the method qualitatively and for different metrics, for both geometric and color reconstruction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Alldieck_Photorealistic_Monocular_3D_Reconstruction_of_Humans_Wearing_Clothing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alldieck_Photorealistic_Monocular_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08906 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alldieck_Photorealistic_Monocular_3D_Reconstruction_of_Humans_Wearing_Clothing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alldieck_Photorealistic_Monocular_3D_Reconstruction_of_Humans_Wearing_Clothing_CVPR_2022_paper.html | CVPR 2022 | null |
A Low-Cost & Real-Time Motion Capture System | Anargyros Chatzitofis, Georgios Albanis, Nikolaos Zioulis, Spyridon Thermos | Traditional marker-based motion capture requires excessive and specialized equipment, hindering accessibility and wider adoption. In this work, we demonstrate such a system but rely on a very sparse set of low-cost consumer-grade sensors. Our system exploits a data-driven backend to infer the captured subject's joint positions from noisy marker estimates in real-time. In addition to reduced costs and portability, its inherent denoising nature allows for quicker captures by alleviating the need for precise marker placement and post-processing, making it suitable for interactive virtual reality applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chatzitofis_A_Low-Cost__Real-Time_Motion_Capture_System_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chatzitofis_A_Low-Cost__Real-Time_Motion_Capture_System_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chatzitofis_A_Low-Cost__Real-Time_Motion_Capture_System_CVPR_2022_paper.html | CVPR 2022 | null |
Unified Contrastive Learning in Image-Text-Label Space | Jianwei Yang, Chunyuan Li, Pengchuan Zhang, Bin Xiao, Ce Liu, Lu Yuan, Jianfeng Gao | Visual recognition is recently learned via either supervised learning on human-annotated image-label data or language-image contrastive learning with webly-crawled image-text pairs. While supervised learning may result in a more discriminative representation, language-image pretraining shows unprecedented zero-shot recognition capability, largely due to the different data sources and learning objectives. In this work, we introduce a new formulation by combining the two data sources into a common image-text-label space. In this new space, we further propose a new learning method, called Unified Contrastive Learning (UniCL) with a single learning objective to seamlessly prompt the synergy between two types of data. Extensive experiments show that our UniCL is an effective way of learning semantically rich yet discriminative representations, universally for zero-shot, linear-probing, fully finetune and transfer learning scenarios. Particularly, it attains gains up to 9.2% and 14.5% in average on zero-shot recognition benchmarks over the language-image contrastive learning and supervised learning methods, respectively. In linear probing setting, it also boosts the performance over the two methods by 7.3% and 3.4%, respectively. Our further study indicates that UniCL is also a good learner on pure image-label data, rivaling the supervised learning methods across three image classification datasets and two types of vision backbone, ResNet and vision Transformer. ResNet and Swin Transformer. Code is available at: https://github.com/microsoft/UniCL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Unified_Contrastive_Learning_in_Image-Text-Label_Space_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Unified_Contrastive_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.03610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unified_Contrastive_Learning_in_Image-Text-Label_Space_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Unified_Contrastive_Learning_in_Image-Text-Label_Space_CVPR_2022_paper.html | CVPR 2022 | null |
Unifying Motion Deblurring and Frame Interpolation With Events | Xiang Zhang, Lei Yu | Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos. To this end, we present a unified framework of event-based motion deblurring and frame interpolation for blurry video enhancement, where the extremely low latency of events is leveraged to alleviate motion blur and facilitate intermediate frame prediction. Specifically, the mapping relation between blurry frames and sharp latent images is first predicted by a learnable double integral network, and a fusion network is then proposed to refine the coarse results via utilizing the information from consecutive blurry inputs and the concurrent events. By exploring the mutual constraints among blurry frames, latent images, and event streams, we further propose a self-supervised learning framework to enable network training with real-world blurry videos and events. Extensive experiments demonstrate that our method compares favorably against the state-of-the-art approaches and achieves remarkable performance on both synthetic and real-world datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Unifying_Motion_Deblurring_and_Frame_Interpolation_With_Events_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Unifying_Motion_Deblurring_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12178 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Unifying_Motion_Deblurring_and_Frame_Interpolation_With_Events_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Unifying_Motion_Deblurring_and_Frame_Interpolation_With_Events_CVPR_2022_paper.html | CVPR 2022 | null |
Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks | Fanqing Lin, Brian Price, Tony Martinez | As deep neural networks become the state-of-the-art approach in the field of computer vision for dense prediction tasks, many methods have been developed for automatic estimation of the target outputs given the visual inputs. Although the estimation accuracy of the proposed automatic methods continues to improve, interactive refinement is oftentimes necessary for further correction. Recently, feature backpropagating refinement scheme (f-BRS) has been proposed for the task of interactive segmentation, which enables efficient optimization of a small set of auxiliary variables inserted into the pretrained network to produce object segmentation that better aligns with user inputs. However, the proposed auxiliary variables only contain channel-wise scale and bias, limiting the optimization to global refinement only. In this work, in order to generalize backpropagating refinement for a wide range of dense prediction tasks, we introduce a set of G-BRS (Generalized Backpropagating Refinement Scheme) layers that enable both global and localized refinement for the following tasks: interactive segmentation, semantic segmentation, image matting and monocular depth estimation. Experiments on SBD, Cityscapes, Mapillary Vista, Composition-1k and NYU-Depth-V2 show that our method can successfully generalize and significantly improve performance of existing pretrained state-of-the-art models with only a few clicks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Generalizing_Interactive_Backpropagating_Refinement_for_Dense_Prediction_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_Generalizing_Interactive_Backpropagating_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Generalizing_Interactive_Backpropagating_Refinement_for_Dense_Prediction_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Generalizing_Interactive_Backpropagating_Refinement_for_Dense_Prediction_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Pre-Training for Temporal Action Localization Tasks | Can Zhang, Tianyu Yang, Junwu Weng, Meng Cao, Jue Wang, Yuexian Zou | Unsupervised video representation learning has made remarkable achievements in recent years. However, most existing methods are designed and optimized for video classification. These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization. To bridge this gap, we make the first attempt to propose a self-supervised pretext task, coined as Pseudo Action Localization (PAL) to Unsupervisedly Pre-train feature encoders for Temporal Action Localization tasks (UP-TAL). Specifically, we first randomly select temporal regions, each of which contains multiple clips, from one video as pseudo actions and then paste them onto different temporal positions of the other two videos. The pretext task is to align the features of pasted pseudo action regions from two synthetic videos and maximize the agreement between them. Compared to the existing unsupervised video representation learning approaches, our PAL adapts better to downstream TAL tasks by introducing a temporal equivariant contrastive learning paradigm in a temporally dense and scale-aware manner. Extensive experiments show that PAL can utilize large-scale unlabeled video data to significantly boost the performance of existing TAL methods. Our codes and models will be made publicly available at https://github.com/zhang-can/UP-TAL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Unsupervised_Pre-Training_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13609 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Unsupervised_Pre-Training_for_Temporal_Action_Localization_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
Light Field Neural Rendering | Mohammed Suhail, Carlos Esteves, Leonid Sigal, Ameesh Makadia | Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects. We introduce a model that combines the strengths and mitigates the limitations of these two directions. By operating on a four-dimensional representation of the light field, our model learns to represent view-dependent effects accurately. By enforcing geometric constraints during training and inference, the scene geometry is implicitly learned from a sparse set of views. Concretely, we introduce a two-stage transformer-based model that first aggregates features along epipolar lines, then aggregates features along reference views to produce the color of a target ray. Our model outperforms the state-of-the-art on multiple forward-facing and 360deg datasets, with larger margins on scenes with severe view-dependent variations. | https://openaccess.thecvf.com/content/CVPR2022/papers/Suhail_Light_Field_Neural_Rendering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Suhail_Light_Field_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09687 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Suhail_Light_Field_Neural_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Suhail_Light_Field_Neural_Rendering_CVPR_2022_paper.html | CVPR 2022 | null |
Fast Point Transformer | Chunghyun Park, Yoonwoo Jeong, Minsu Cho, Jaesik Park | The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This paper introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel based method, and our network achieves 129 times faster inference time than the state-of-the-art, Point Transformer, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Fast_Point_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Fast_Point_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04702 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Fast_Point_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Fast_Point_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Look Outside the Room: Synthesizing a Consistent Long-Term 3D Scene Video From a Single Image | Xuanchi Ren, Xiaolong Wang | Novel view synthesis from a single image has recently attracted a lot of attention, and it has been primarily advanced by 3D deep learning and rendering techniques. However, most work is still limited by synthesizing new views within relatively small camera motions. In this paper, we propose a novel approach to synthesize a consistent long-term video given a single scene image and a trajectory of large camera motions. Our approach utilizes an autoregressive Transformer to perform sequential modeling of multiple frames, which reasons the relations between multiple frames and the corresponding cameras to predict the next frame. To facilitate learning and ensure consistency among generated frames, we introduce a locality constraint based on the input cameras to guide self-attention among a large number of patches across space and time. Our method outperforms state-of-the-art view synthesis approaches by a large margin, especially when synthesizing long-term future in indoor 3D scenes. Project page at https://xrenaa.github.io/look-outside-room/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Look_Outside_the_Room_Synthesizing_a_Consistent_Long-Term_3D_Scene_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_Look_Outside_the_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09457 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Look_Outside_the_Room_Synthesizing_a_Consistent_Long-Term_3D_Scene_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Look_Outside_the_Room_Synthesizing_a_Consistent_Long-Term_3D_Scene_CVPR_2022_paper.html | CVPR 2022 | null |
Unimodal-Concentrated Loss: Fully Adaptive Label Distribution Learning for Ordinal Regression | Qiang Li, Jingjing Wang, Zhaoliang Yao, Yachun Li, Pengju Yang, Jingwei Yan, Chunmao Wang, Shiliang Pu | Learning from a label distribution has achieved promising results on ordinal regression tasks such as facial age and head pose estimation wherein, the concept of adaptive label distribution learning (ALDL) has drawn lots of attention recently for its superiority in theory. However, compared with the methods assuming fixed form label distribution, ALDL methods have not achieved better performance. We argue that existing ALDL algorithms do not fully exploit the intrinsic properties of ordinal regression. In this paper, we emphatically summarize that learning an adaptive label distribution on ordinal regression tasks should follow three principles. First, the probability corresponding to the ground-truth should be the highest in label distribution. Second, the probabilities of neighboring labels should decrease with the increase of distance away from the ground-truth, i.e., the distribution is unimodal. Third, the label distribution should vary with samples changing, and even be distinct for different instances with the same label, due to the different levels of difficulty and ambiguity. Under the premise of these principles, we propose a novel loss function for fully adaptive label distribution learning, namely unimodal-concentrated loss. Specifically, the unimodal loss derived from the learning to rank strategy constrains the distribution to be unimodal. Furthermore, the estimation error and the variance of the predicted distribution for a specific sample are integrated into the proposed concentrated loss to make the predicted distribution maximize at the ground-truth and vary according to the predicting uncertainty. Extensive experimental results on typical ordinal regression tasks including age and head pose estimation, show the superiority of our proposed unimodal-concentrated loss compared with existing loss functions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Unimodal-Concentrated_Loss_Fully_Adaptive_Label_Distribution_Learning_for_Ordinal_Regression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Unimodal-Concentrated_Loss_Fully_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00309 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Unimodal-Concentrated_Loss_Fully_Adaptive_Label_Distribution_Learning_for_Ordinal_Regression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Unimodal-Concentrated_Loss_Fully_Adaptive_Label_Distribution_Learning_for_Ordinal_Regression_CVPR_2022_paper.html | CVPR 2022 | null |
Augmented Geometric Distillation for Data-Free Incremental Person ReID | Yichen Lu, Mei Wang, Weihong Deng | Incremental learning (IL) remains an open issue for Person Re-identification (ReID), where a ReID system is expected to preserve preceding knowledge while learning incrementally. However, due to the strict privacy licenses and the open-set retrieval setting, it is intractable to adapt existing class IL methods to ReID. In this work, we propose an Augmented Geometric Distillation (AGD) framework to tackle these issues. First, a general data-free incremental framework with dreaming memory is constructed to avoid privacy disclosure. On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it. Second, for the open-set retrieval property, we propose to maintain feature space structure during evolving via a novel geometric way and preserve relationships between exemplars when representations drift. Extensive experiments demonstrate the superiority of our AGD to baseline with a margin of 6.0% mAP / 7.9% R@1 and it could be generalized to class IL. Code is available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_Augmented_Geometric_Distillation_for_Data-Free_Incremental_Person_ReID_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_Augmented_Geometric_Distillation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Augmented_Geometric_Distillation_for_Data-Free_Incremental_Person_ReID_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_Augmented_Geometric_Distillation_for_Data-Free_Incremental_Person_ReID_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Stereo Image Compression via Bi-Directional Coding | Jianjun Lei, Xiangrui Liu, Bo Peng, Dengchao Jin, Wanqing Li, Jingxiao Gu | Existing learning-based stereo compression methods usually adopt a unidirectional approach to encoding one image independently and the other image conditioned upon the first. This paper proposes a novel bi-directional coding-based end-to-end stereo image compression network (BCSIC-Net). BCSIC-Net consists of a novel bi-directional contextual transform module which performs nonlinear transform conditioned upon the inter-view context in a latent space to reduce inter-view redundancy, and a bi-directional conditional entropy model that employs inter-view correspondence as a conditional prior to improve coding efficiency. Experimental results on the InStereo2K and KITTI datasets demonstrate that the proposed BCSIC-Net can effectively reduce the inter-view redundancy and outperforms state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lei_Deep_Stereo_Image_Compression_via_Bi-Directional_Coding_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Deep_Stereo_Image_Compression_via_Bi-Directional_Coding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lei_Deep_Stereo_Image_Compression_via_Bi-Directional_Coding_CVPR_2022_paper.html | CVPR 2022 | null |
Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems Through Stochastic Contraction | Hyungjin Chung, Byeongsu Sim, Jong Chul Ye | Diffusion models have recently attained significant interest within the community owing to their strong performance as generative models. Furthermore, its application to inverse problems have demonstrated state-of-the-art performance. Unfortunately, diffusion models have a critical downside - they are inherently slow to sample from, needing few thousand steps of iteration to generate images from pure Gaussian noise. In this work, we show that starting from Gaussian noise is unnecessary. Instead, starting from a single forward diffusion with better initialization significantly reduces the number of sampling steps in the reverse conditional diffusion. This phenomenon is formally explained by the contraction theory of the stochastic difference equations like our conditional diffusion strategy - the alternating applications of reverse diffusion followed by a non-expansive data consistency step. The new sampling strategy, dubbed Come-Closer-Diffuse-Faster (CCDF), also reveals a new insight on how the existing feed-forward neural network approaches for inverse problems can be synergistically combined with the diffusion models. Experimental results with super-resolution, image inpainting, and compressed sensing MRI demonstrate that our method can achieve state-of-the-art reconstruction performance at significantly reduced sampling steps. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chung_Come-Closer-Diffuse-Faster_Accelerating_Conditional_Diffusion_Models_for_Inverse_Problems_Through_Stochastic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chung_Come-Closer-Diffuse-Faster_Accelerating_Conditional_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chung_Come-Closer-Diffuse-Faster_Accelerating_Conditional_Diffusion_Models_for_Inverse_Problems_Through_Stochastic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chung_Come-Closer-Diffuse-Faster_Accelerating_Conditional_Diffusion_Models_for_Inverse_Problems_Through_Stochastic_CVPR_2022_paper.html | CVPR 2022 | null |
Smooth-Swap: A Simple Enhancement for Face-Swapping With Smoothness | Jiseob Kim, Jihoon Lee, Byoung-Tak Zhang | Face-swapping models have been drawing attention for their compelling generation quality, but their complex architectures and loss functions often require careful tuning for successful training. We propose a new face-swapping model called 'Smooth-Swap', which excludes complex handcrafted designs and allows fast and stable training. The main idea of Smooth-Swap is to build smooth identity embedding that can provide stable gradients for identity change. Unlike the one used in previous models trained for a purely discriminative task, the proposed embedding is trained with a supervised contrastive loss promoting a smoother space. With improved smoothness, Smooth-Swap suffices to be composed of a generic U-Net-based generator and three basic loss functions, a far simpler design compared with the previous models. Extensive experiments on face-swapping benchmarks (FFHQ, FaceForensics++) and face images in the wild show that our model is also quantitatively and qualitatively comparable or even superior to the existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Smooth-Swap_A_Simple_Enhancement_for_Face-Swapping_With_Smoothness_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Smooth-Swap_A_Simple_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Smooth-Swap_A_Simple_Enhancement_for_Face-Swapping_With_Smoothness_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Smooth-Swap_A_Simple_Enhancement_for_Face-Swapping_With_Smoothness_CVPR_2022_paper.html | CVPR 2022 | null |
Full-Range Virtual Try-On With Recurrent Tri-Level Transform | Han Yang, Xinrui Yu, Ziwei Liu | Virtual try-on aims to transfer a target clothing image onto a reference person. Though great progress has been achieved, the functioning zone of existing works is still limited to standard clothes (e.g., plain shirt without complex laces or ripped effect), while the vast complexity and variety of non-standard clothes (e.g., off-shoulder shirt, word-shoulder dress) are largely ignored. In this work, we propose a principled framework, Recurrent Tri-Level Transform (RT-VTON), that performs full-range virtual try-on on both standard and non-standard clothes. We have two key insights towards the framework design: 1) Semantics transfer requires a gradual feature transform on three different levels of clothing representations, namely clothes code, pose code and parsing code. 2) Geometry transfer requires a regularized image deformation between rigidity and flexibility. Firstly, we predict the semantics of the "after-try-on" person by recurrently refining the tri-level feature codes using local gated attention and non-local correspondence learning. Next, we design a semi-rigid deformation to align the clothing image and the predicted semantics, which preserves local warping similarity. Finally, a canonical try-on synthesizer fuses all the processed information to generate the clothed person image. Extensive experiments on conventional benchmarks along with user studies demonstrate that our framework achieves state-of-the-art performance both quantitatively and qualitatively. Notably, RT-VTON shows compelling results on a wide range of non-standard clothes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Full-Range_Virtual_Try-On_With_Recurrent_Tri-Level_Transform_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Full-Range_Virtual_Try-On_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Full-Range_Virtual_Try-On_With_Recurrent_Tri-Level_Transform_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Full-Range_Virtual_Try-On_With_Recurrent_Tri-Level_Transform_CVPR_2022_paper.html | CVPR 2022 | null |
Style Neophile: Constantly Seeking Novel Styles for Domain Generalization | Juwon Kang, Sohyun Lee, Namyup Kim, Suha Kwak | This paper studies domain generalization via domain-invariant representation learning. Existing methods in this direction suppose that a domain can be characterized by styles of its images, and train a network using style-augmented data so that the network is not biased to particular style distributions. However, these methods are restricted to a finite set of styles since they obtain styles for augmentation from a fixed set of external images or by interpolating those of training data. To address this limitation and maximize the benefit of style augmentation, we propose a new method that synthesizes novel styles constantly during training. Our method manages multiple queues to store styles that have been observed so far, and synthesizes novel styles whose distribution is distinct from the distribution of styles in the queues. The style synthesis process is formulated as a monotone submodular optimization, thus can be conducted efficiently by a greedy algorithm. Extensive experiments on four public benchmarks demonstrate that the proposed method is capable of achieving state-of-the-art domain generalization performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kang_Style_Neophile_Constantly_Seeking_Novel_Styles_for_Domain_Generalization_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_Style_Neophile_Constantly_Seeking_Novel_Styles_for_Domain_Generalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kang_Style_Neophile_Constantly_Seeking_Novel_Styles_for_Domain_Generalization_CVPR_2022_paper.html | CVPR 2022 | null |
High-Fidelity Human Avatars From a Single RGB Camera | Hao Zhao, Jinsong Zhang, Yu-Kun Lai, Zerong Zheng, Yingdi Xie, Yebin Liu, Kun Li | In this paper, we propose a coarse-to-fine framework to reconstruct a personalized high-fidelity human avatar from a monocular video. To deal with the misalignment problem caused by the changed poses and shapes in different frames, we design a dynamic surface network to recover pose-dependent surface deformations, which help to decouple the shape and texture of the person. To cope with the complexity of textures and generate photo-realistic results, we propose a reference-based neural rendering network and exploit a bottom-up sharpening-guided fine-tuning strategy to obtain detailed textures. Our framework also enables photo-realistic novel view/pose synthesis and shape editing applications. Experimental results on both the public dataset and our collected dataset demonstrate that our method outperforms the state-of-the-art methods. The code and dataset will be available at http://cic.tju.edu.cn/faculty/likun/projects/HF-Avatar. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_High-Fidelity_Human_Avatars_From_a_Single_RGB_Camera_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_High-Fidelity_Human_Avatars_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_High-Fidelity_Human_Avatars_From_a_Single_RGB_Camera_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_High-Fidelity_Human_Avatars_From_a_Single_RGB_Camera_CVPR_2022_paper.html | CVPR 2022 | null |
ADAPT: Vision-Language Navigation With Modality-Aligned Action Prompts | Bingqian Lin, Yi Zhu, Zicong Chen, Xiwen Liang, Jianzhuang Liu, Xiaodan Liang | Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi-modal inputs. In this paper, we propose modAlity-aligneD Action PrompTs (ADAPT), which provides the VLN agent with action prompts to enable the explicit learning of action-level modality alignment to pursue successful navigation. Specifically, an action prompt is defined as a modality-aligned pair of an image sub-prompt and a text sub-prompt, where the former is a single-view observation and the latter is a phrase like "walk past the chair". When starting navigation, the instruction-related action prompt set is retrieved from a pre-built action prompt base and passed through a prompt encoder to obtain the prompt feature. Then the prompt feature is concatenated with the original instruction feature and fed to a multi-layer transformer for action prediction. To collect high-quality action prompts into the prompt base, we use the Contrastive Language-Image Pretraining (CLIP) model which has powerful cross-modality alignment ability. A modality alignment loss and a sequential consistency loss are further introduced to enhance the alignment of the action prompt and enforce the agent to focus on the related prompt sequentially. Experimental results on both R2R and RxR show the superiority of ADAPT over state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_ADAPT_Vision-Language_Navigation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.15509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_ADAPT_Vision-Language_Navigation_With_Modality-Aligned_Action_Prompts_CVPR_2022_paper.html | CVPR 2022 | null |
Multiview Transformers for Video Recognition | Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, Cordelia Schmid | Video understanding requires reasoning at multiple spatiotemporal resolutions -- from short fine-grained motions to events taking place over longer durations. Although transformer architectures have recently advanced the state-of-the-art, they have not explicitly modelled different spatiotemporal resolutions. To this end, we present Multiview Transformers for Video Recognition (MTV). Our model consists of separate encoders to represent different views of the input video with lateral connections to fuse information across views. We present thorough ablation studies of our model and show that MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes. Furthermore, we achieve state-of-the-art results on six standard datasets, and improve even further with large-scale pretaining. Code and checkpoints are available at: https://github.com/google-research/scenic. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Multiview_Transformers_for_Video_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yan_Multiview_Transformers_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04288 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Multiview_Transformers_for_Video_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yan_Multiview_Transformers_for_Video_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
RIO: Rotation-Equivariance Supervised Learning of Robust Inertial Odometry | Xiya Cao, Caifa Zhou, Dandan Zeng, Yongliang Wang | This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry models. We demonstrate that the self-supervised scheme provides a powerful supervisory signal at training phase as well as at inference stage. It reduces the reliance on massive amounts of labeled data for training a robust model and makes it possible to update the model using various unlabeled data. Further, we propose adaptive Test-Time Training (TTT) based on uncertainty estimations in order to enhance the generalizability of the inertial odometry to various unseen data. We show in experiments that the Rotation-equivariance-supervised Inertial Odometry (RIO) trained with 30% data achieves on par performance with a model trained with the whole database. Adaptive TTT improves models performance in all cases and makes more than 25% improvements under several scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_RIO_Rotation-Equivariance_Supervised_Learning_of_Robust_Inertial_Odometry_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cao_RIO_Rotation-Equivariance_Supervised_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.11676 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_RIO_Rotation-Equivariance_Supervised_Learning_of_Robust_Inertial_Odometry_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_RIO_Rotation-Equivariance_Supervised_Learning_of_Robust_Inertial_Odometry_CVPR_2022_paper.html | CVPR 2022 | null |
How Good Is Aesthetic Ability of a Fashion Model? | Xingxing Zou, Kaicheng Pang, Wen Zhang, Waikeung Wong | We introduce A100 (Aesthetic 100) to assess the aesthetic ability of the fashion compatibility models. To date, it is the first work to address the AI model's aesthetic ability with detailed characterization based on the professional fashion domain knowledge. A100 has several desirable characteristics: 1. Completeness. It covers all types of standards in the fashion aesthetic system through two tests, namely LAT (Liberalism Aesthetic Test) and AAT (Academicism Aesthetic Test); 2. Reliability. It is training data agnostic and consistent with major indicators. It provides a fair and objective judgment for model comparison. 3. Explainability. Better than all previous indicators, the A100 further identifies essential characteristics of fashion aesthetics, thus showing the model's performance on more fine-grained dimensions, such as Color, Balance, Material, etc. Experimental results prove the advance of the A100 in the aforementioned aspects. All data can be found at https://github.com/AemikaChow/AiDLab- fAshIon-Data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zou_How_Good_Is_Aesthetic_Ability_of_a_Fashion_Model_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zou_How_Good_Is_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_How_Good_Is_Aesthetic_Ability_of_a_Fashion_Model_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zou_How_Good_Is_Aesthetic_Ability_of_a_Fashion_Model_CVPR_2022_paper.html | CVPR 2022 | null |
Mining Multi-View Information: A Strong Self-Supervised Framework for Depth-Based 3D Hand Pose and Mesh Estimation | Pengfei Ren, Haifeng Sun, Jiachang Hao, Jingyu Wang, Qi Qi, Jianxin Liao | In this work, we study the cross-view information fusion problem in the task of self-supervised 3D hand pose estimation from the depth image. Previous methods usually adopt a hand-crafted rule to generate pseudo labels from multi-view estimations in order to supervise the network training in each view. However, these methods ignore the rich semantic information in each view and ignore the complex dependencies between different regions of different views. To solve these problems, we propose a cross-view fusion network to fully exploit and adaptively aggregate multi-view information. We encode diverse semantic information in each view into multiple compact nodes. Then, we introduce the graph convolution to model the complex dependencies between nodes and perform cross-view information interaction. Based on the cross-view fusion network, we propose a strong self-supervised framework for 3D hand pose and hand mesh estimation. Furthermore, we propose a pseudo multi-view training strategy to extend our framework to a more general scenario in which only single-view training data is used. Results on NYU dataset demonstrate that our method outperforms the previous self-supervised methods by 17.5% and 30.3% in multi-view and single-view scenarios. Meanwhile, our framework achieves comparable results to several strongly supervised methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_Mining_Multi-View_Information_A_Strong_Self-Supervised_Framework_for_Depth-Based_3D_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_Mining_Multi-View_Information_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Mining_Multi-View_Information_A_Strong_Self-Supervised_Framework_for_Depth-Based_3D_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_Mining_Multi-View_Information_A_Strong_Self-Supervised_Framework_for_Depth-Based_3D_CVPR_2022_paper.html | CVPR 2022 | null |
Automated Progressive Learning for Efficient Training of Vision Transformers | Changlin Li, Bohan Zhuang, Guangrun Wang, Xiaodan Liang, Xiaojun Chang, Yi Yang | Recent advances in vision Transformers (ViTs) have come with a voracious appetite for computing power, high-lighting the urgent need to develop efficient training methods for ViTs. Progressive learning, a training scheme where the model capacity grows progressively during training, has started showing its ability in efficient training. In this paper, we take a practical step towards efficient training of ViTs by customizing and automating progressive learning. First, we develop a strong manual baseline for progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge the gap brought by model growth. Then, we propose automated progressive learning (AutoProg), an efficient training scheme that aims to achieve lossless acceleration by automatically increasing the training overload on-the-fly; this is achieved by adaptively deciding whether, where and how much should the model grow during progressive learning. Specifically, we first relax the optimization of the growth schedule to sub-network architecture optimization problem, then propose one-shot estimation of the sub-network performance via an elastic supernet. The searching overhead is reduced to minimal by recycling the parameters of the supernet. Extensive experiments of efficient training on ImageNet with two representative ViT models, DeiT and VOLO, demonstrate that AutoProg can accelerate ViTs training by up to 85.1% with no performance drop. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Automated_Progressive_Learning_for_Efficient_Training_of_Vision_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Automated_Progressive_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Automated_Progressive_Learning_for_Efficient_Training_of_Vision_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Automated_Progressive_Learning_for_Efficient_Training_of_Vision_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
BTS: A Bi-Lingual Benchmark for Text Segmentation in the Wild | Xixi Xu, Zhongang Qi, Jianqi Ma, Honglun Zhang, Ying Shan, Xiaohu Qie | As a prerequisite of many text-related tasks such as text erasing and text style transfer, text segmentation arouses more and more attention recently. Current researches mainly focus on only English characters and digits, while few work studies Chinese characters due to the lack of public large-scale and high-quality Chinese datasets, which limits the practical application scenarios of text segmentation. Different from English which has a limited alphabet of letters, Chinese has much more basic characters with complex structures, making the problem more difficult to deal with. To better analyze this problem, we propose the Bi-lingual Text Segmentation (BTS) dataset, a benchmark that covers various common Chinese scenes including 14,250 diverse and fine-annotated text images. BTS mainly focuses on Chinese characters, and also contains English words and digits. We also introduce Prior Guided Text Segmentation Network (PGTSNet), the first baseline to handle bi-lingual and complex-structured text segmentation. A plug-in text region highlighting module and a text perceptual discriminator are proposed in PGTSNet to supervise the model with text prior, and guide for more stable and finer text segmentation. A variation loss is also employed for suppressing background noise under complex scene. Extensive experiments are conducted not only to demonstrate the necessity and superiority of the proposed dataset BTS, but also to show the effectiveness of the proposed PGTSNet compared with a variety of state-of-the-art text segmentation methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_BTS_A_Bi-Lingual_Benchmark_for_Text_Segmentation_in_the_Wild_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_BTS_A_Bi-Lingual_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_BTS_A_Bi-Lingual_Benchmark_for_Text_Segmentation_in_the_Wild_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_BTS_A_Bi-Lingual_Benchmark_for_Text_Segmentation_in_the_Wild_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Structured Gaussians To Approximate Deep Ensembles | Ivor J. A. Simpson, Sara Vicente, Neill D. F. Campbell | This paper proposes using a sparse-structured multivariate Gaussian to provide a closed-form approximator for the output of probabilistic ensemble models used for dense image prediction tasks. This is achieved through a convolutional neural network that predicts the mean and covariance of the distribution, where the inverse covariance is parameterised by a sparsely structured Cholesky matrix. Similarly to distillation approaches, our single network is trained to maximise the probability of samples from pre-trained probabilistic models, in this work we use a fixed ensemble of networks. Once trained, our compact representation can be used to efficiently draw spatially correlated samples from the approximated output distribution. Importantly, this approach captures the uncertainty and structured correlations in the predictions explicitly in a formal distribution, rather than implicitly through sampling alone. This allows direct introspection of the model, enabling visualisation of the learned structure. Moreover, this formulation provides two further benefits: estimation of a sample probability, and the introduction of arbitrary spatial conditioning at test time. We demonstrate the merits of our approach on monocular depth estimation and show that the advantages of our approach are obtained with comparable quantitative performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Simpson_Learning_Structured_Gaussians_To_Approximate_Deep_Ensembles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Simpson_Learning_Structured_Gaussians_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.15485 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Simpson_Learning_Structured_Gaussians_To_Approximate_Deep_Ensembles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Simpson_Learning_Structured_Gaussians_To_Approximate_Deep_Ensembles_CVPR_2022_paper.html | CVPR 2022 | null |
Adaptive Trajectory Prediction via Transferable GNN | Yi Xu, Lichen Wang, Yizhou Wang, Yun Fu | Pedestrian trajectory prediction is an essential component in a wide range of AI applications such as autonomous driving and robotics. Existing methods usually assume the training and testing motions follow the same pattern while ignoring the potential distribution differences (e.g., shopping mall and street). This issue results in inevitable performance decrease. To address this issue, we propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain-invariant GNN is proposed to explore the structural motion knowledge where the domain-specific knowledge is reduced. Moreover, an attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representations for knowledge transfer. By this way, disparities across different trajectory domains will be better alleviated. More challenging while practical trajectory prediction experiments are designed, and the experimental results verify the superior performance of our proposed model. To the best of our knowledge, our work is the pioneer which fills the gap in benchmarks and techniques for practical pedestrian trajectory prediction across different domains. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Adaptive_Trajectory_Prediction_via_Transferable_GNN_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Adaptive_Trajectory_Prediction_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Adaptive_Trajectory_Prediction_via_Transferable_GNN_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Adaptive_Trajectory_Prediction_via_Transferable_GNN_CVPR_2022_paper.html | CVPR 2022 | null |
Total Variation Optimization Layers for Computer Vision | Raymond A. Yeh, Yuan-Ting Hu, Zhongzheng Ren, Alexander G. Schwing | Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?; (b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly-supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results, we had to address question (b): we developed a GPU-based projected-Newton method which is 37x faster than existing solutions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yeh_Total_Variation_Optimization_Layers_for_Computer_Vision_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.03643 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_Total_Variation_Optimization_Layers_for_Computer_Vision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yeh_Total_Variation_Optimization_Layers_for_Computer_Vision_CVPR_2022_paper.html | CVPR 2022 | null |
Defensive Patches for Robust Recognition in the Physical World | Jiakai Wang, Zixin Yin, Pengfei Hu, Aishan Liu, Renshuai Tao, Haotong Qin, Xianglong Liu, Dacheng Tao | To operate in real-world high-stakes environments, deep learning systems have to endure noises that have been continuously thwarting their robustness. Data-end defense, which improves robustness by operations on input data instead of modifying models, has attracted intensive attention due to its high feasibility in practice. However, previous data-end defenses show low generalization against diverse noises and weak transferability across multiple models. Motivated by the fact that robust recognition depends on both local and global features, we propose a defensive patch generation framework to address these problems by helping models better exploit these features. For the generalization against diverse noises, we inject class-specific identifiable patterns into a confined local patch prior, so that defensive patches could preserve more recognizable features towards specific classes, leading models for better recognition under noises. For the transferability across multiple models, we guide the defensive patches to capture more global feature correlations within a class, so that they could activate model-shared global perceptions and transfer better among models. Our defensive patches show great potentials to improve model robustness in practice by simply sticking them around target objects. Extensive experiments show that we outperform others by large margins (improve 20+% accuracy for both adversarial and corruption robustness on average in the digital and physical world). | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Defensive_Patches_for_Robust_Recognition_in_the_Physical_World_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Defensive_Patches_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.06213 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Defensive_Patches_for_Robust_Recognition_in_the_Physical_World_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Defensive_Patches_for_Robust_Recognition_in_the_Physical_World_CVPR_2022_paper.html | CVPR 2022 | null |
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation | Lei Jin, Chenyang Xu, Xiaojuan Wang, Yabo Xiao, Yandong Guo, Xuecheng Nie, Jian Zhao | The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i.e., top-down or bottom-up, leading to redundant pipelines with high computation cost. We argue that it is more desirable to simplify such two-stage paradigm to a single-stage one to promote both efficiency and performance. To this end, we present an efficient single-stage solution, Decoupled Regression Model (DRM), with three distinct novelties. First, DRM introduces a new decoupled representation for 3D pose, which expresses the 2D pose in image plane and depth information of each 3D human instance via 2D center point (center of visible keypoints) and root point (denoted as pelvis), respectively. Second, to learn better feature representation for the human depth regression, DRM introduces a 2D Pose-guided Depth Query Module (PDQM) to extract the features in 2D pose regression branch, enabling the depth regression branch to perceive the scale information of instances. Third, DRM leverages a Decoupled Absolute Pose Loss (DAPL) to facilitate the absolute root depth and root-relative depth estimation, thus improving the accuracy of absolute 3D pose. Comprehensive experiments on challenging benchmarks including MuPoTS-3D and Panoptic clearly verify the superiority of our framework, which outperforms the state-of-the-art bottom-up absolute 3D pose estimation methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Single-Stage_Is_Enough_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Single-Stage_Is_Enough_Multi-Person_Absolute_3D_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds | Zhao Jin, Yinjie Lei, Naveed Akhtar, Haifeng Li, Munawar Hayat | Point cloud scene flow estimation is of practical importance for dynamic scene navigation in autonomous driving. Since scene flow labels are hard to obtain, current methods train their models on synthetic data and transfer them to real scenes. However, large disparities between existing synthetic datasets and real scenes lead to poor model transfer. We make two major contributions to address that. First, we develop a point cloud collector and scene flow annotator for GTA-V engine to automatically obtain diverse realistic training samples without human intervention. With that, we develop a large-scale synthetic scene flow dataset GTA-SF. Second, we propose a mean-teacher-based domain adaptation framework that leverages self-generated pseudo-labels of the target domain. It also explicitly incorporates shape deformation regularization and surface correspondence refinement to address distortions and misalignments in domain transfer. Through extensive experiments, we show that our GTA-SF dataset leads to a consistent boost in model generalization to three real datasets (i.e., Waymo, Lyft and KITTI) as compared to the most widely used FT3D dataset. Moreover, our framework achieves superior adaptation performance on six source-target dataset pairs, remarkably closing the average domain gap by 60%. Data and codes are available at https://github.com/leolyj/DCA-SRSFE | https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Deformation_and_Correspondence_Aware_Unsupervised_Synthetic-to-Real_Scene_Flow_Estimation_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jin_Deformation_and_Correspondence_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16895 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Deformation_and_Correspondence_Aware_Unsupervised_Synthetic-to-Real_Scene_Flow_Estimation_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jin_Deformation_and_Correspondence_Aware_Unsupervised_Synthetic-to-Real_Scene_Flow_Estimation_for_CVPR_2022_paper.html | CVPR 2022 | null |
Learn From Others and Be Yourself in Heterogeneous Federated Learning | Wenke Huang, Mang Ye, Bo Du | Federated learning has emerged as an important distributed learning paradigm, which normally involves collaborative updating with others and local updating on private data. However, heterogeneity problem and catastrophic forgetting bring distinctive challenges. First, due to non-i.i.d (identically and independently distributed) data and heterogeneous architectures, models suffer performance degradation on other domains and communication barrier with participants models. Second, in local updating, model is separately optimized on private data, which is prone to overfit current data distribution and forgets previously acquired knowledge, resulting in catastrophic forgetting. In this work, we propose FCCL (Federated Cross-Correlation and Continual Learning). For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Meanwhile, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. Empirical results on various image classification tasks demonstrate the effectiveness of our method and the efficiency of modules. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Sequential Voting With Relational Box Fields for Active Object Detection | Qichen Fu, Xingyu Liu, Kris Kitani | A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand. In order to accurately localize the active object, any method must reason using information encoded by each image pixel, such as whether it belongs to the hand, the object, or the background. To leverage each pixel as evidence to determine the bounding box of the active object, we propose a pixel-wise voting function. Our pixel-wise voting function takes an initial bounding box as input and produces an improved bounding box of the active object as output. The voting function is designed so that each pixel inside of the input bounding box votes for an improved bounding box, and the box with the majority vote is selected as the output. We call the collection of bounding boxes generated inside of the voting function, the Relational Box Field, as it characterizes a field of bounding boxes defined in relationship to the current bounding box. While our voting function is able to improve the bounding box of the active object, one round of voting is typically not enough to accurately localize the active object. Therefore, we repeatedly apply the voting function to sequentially improve the location of the bounding box. However, since it is known that repeatedly applying a one-step predictor (i.e., auto-regressive processing with our voting function) can cause a data distribution shift, we mitigate this issue using reinforcement learning (RL). We adopt standard RL to learn the voting function parameters and show that it provides a meaningful improvement over a standard supervised learning approach. We perform experiments on two large-scale datasets: 100DOH and MECCANO, improving AP50 performance by 8% and 30%, respectively, over the state of the art. The project page with code and visualizations can be found at https://fuqichen1998.github.io/SequentialVotingDet/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fu_Sequential_Voting_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.11524 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fu_Sequential_Voting_With_Relational_Box_Fields_for_Active_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Semantic-Aware Auto-Encoders for Self-Supervised Representation Learning | Guangrun Wang, Yansong Tang, Liang Lin, Philip H.S. Torr | The resurgence of unsupervised learning can be attributed to the remarkable progress of self-supervised learning, which includes generative (G) and discriminative (D) models. In computer vision, the mainstream self-supervised learning algorithms are D models. However, designing a D model could be over-complicated; also, some studies hinted that a D model might not be as general and interpretable as a G model. In this paper, we switch from D models to G models using the classical auto-encoder (AE). Note that a vanilla G model was far less efficient than a D model in self-supervised computer vision tasks, as it wastes model capability on overfitting semantic-agnostic high-frequency details. Inspired by perceptual learning that could use cross-view learning to perceive concepts and semantics, we propose a novel AE that could learn semantic-aware representation via cross-view image reconstruction. We use one view of an image as the input and another view of the same image as the reconstruction target. This kind of AE has rarely been studied before, and the optimization is very difficult. To enhance learning ability and find a feasible solution, we propose a semantic aligner that uses geometric transformation knowledge to align the hidden code of AE to help optimization. These techniques significantly improve the representation learning ability of AE and make self-supervised learning with G models possible. Extensive experiments on many large-scale benchmarks (e.g., ImageNet, COCO 2017, and SYSU-30k) demonstrate the effectiveness of our methods. Code is available at https://github.com/wanggrun/Semantic-Aware-AE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Semantic-Aware_Auto-Encoders_for_Self-Supervised_Representation_Learning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Semantic-Aware_Auto-Encoders_for_Self-Supervised_Representation_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Semantic-Aware_Auto-Encoders_for_Self-Supervised_Representation_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Transferable Human-Object Interaction Detector With Natural Language Supervision | Suchen Wang, Yueqi Duan, Henghui Ding, Yap-Peng Tan, Kim-Hui Yap, Junsong Yuan | It is difficult to construct a data collection including all possible combinations of human actions and interacting objects due to the combinatorial nature of human-object interactions (HOI). In this work, we aim to develop a transferable HOI detector for unseen interactions. Existing HOI detectors often treat interactions as discrete labels and learn a classifier according to a predetermined category space. This is inherently inapt for detecting unseen interactions which are out of the predefined categories. Conversely, we treat independent HOI labels as the natural language supervision of interactions and embed them into a joint visual-and-text space to capture their correlations. More specifically, we propose a new HOI visual encoder to detect the interacting humans and objects, and map them to a joint feature space to perform interaction recognition. Our visual encoder is instantiated as a Vision Transformer with new learnable HOI tokens and a sequence parser to generate unique HOI predictions. It distills and leverages the transferable knowledge from the pretrained CLIP model to perform the zero-shot interaction detection. Experiments on two datasets, SWIG-HOI and HICO-DET, validate that our proposed method can achieve a notable mAP improvement on detecting both seen and unseen HOIs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Learning_Transferable_Human-Object_Interaction_Detector_With_Natural_Language_Supervision_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_Transferable_Human-Object_Interaction_Detector_With_Natural_Language_Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Learning_Transferable_Human-Object_Interaction_Detector_With_Natural_Language_Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
Fourier Document Restoration for Robust Document Dewarping and Recognition | Chuhui Xue, Zichen Tian, Fangneng Zhan, Shijian Lu, Song Bai | State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth. This paper presents FDRNet, a Fourier Document Restoration Network that can restore documents with different distortions and improve document recognition in a reliable and simpler manner. FDRNet focuses on high-frequency components in the Fourier space that capture most structural information but are largely free of degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline transformation which can handle various deformations effectively without requiring deformation annotations in training. These features allow FDRNet to learn from a small amount of simply labeled training images, and the learned model can dewarp documents with complex geometric distortion and recognize the restored texts accurately. To facilitate document restoration research, we create a benchmark dataset consisting of over one thousand camera documents with different types of geometric and photometric distortion. Extensive experiments show that FDRNet outperforms the state-of-the-art by large margins on both dewarping and text recognition tasks. In addition, FDRNet requires a small amount of simply labeled training data and is easy to deploy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xue_Fourier_Document_Restoration_for_Robust_Document_Dewarping_and_Recognition_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.09910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Fourier_Document_Restoration_for_Robust_Document_Dewarping_and_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xue_Fourier_Document_Restoration_for_Robust_Document_Dewarping_and_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection | Jihwan Park, SeungJun Lee, Hwan Heo, Hyeong Kyu Choi, Hyunwoo J. Kim | Human-Object Interaction detection is a holistic visual recognition task that entails object detection as well as interaction classification. Previous works of HOI detection has been addressed by the various compositions of subset predictions, e.g., Image -> HO -> I, Image -> HI -> O. Recently, transformer based architecture for HOI has emerged, which directly predicts the HOI triplets in an end-to-end fashion (Image -> HOI). Motivated by various inference paths for HOI detection, we propose cross-path consistency learning (CPC), which is a novel end-to-end learning strategy to improve HOI detection for transformers by leveraging augmented decoding paths. CPC learning enforces all the possible predictions from permuted inference sequences to be consistent. This simple scheme makes the model learn consistent representations, thereby improving generalization without increasing model capacity. Our experiments demonstrate the effectiveness of our method, and we achieved significant improvement on V-COCO and HICO-DET compared to the baseline models. Our code is available at https://github.com/mlvlab/CPChoi. | https://openaccess.thecvf.com/content/CVPR2022/papers/Park_Consistency_Learning_via_Decoding_Path_Augmentation_for_Transformers_in_Human_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Park_Consistency_Learning_via_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Consistency_Learning_via_Decoding_Path_Augmentation_for_Transformers_in_Human_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Park_Consistency_Learning_via_Decoding_Path_Augmentation_for_Transformers_in_Human_CVPR_2022_paper.html | CVPR 2022 | null |
Consistent Explanations by Contrastive Learning | Vipin Pillai, Soroush Abbasi Koohpayegani, Ashley Ouligian, Dennis Fong, Hamed Pirsiavash | Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanations are not always consistent with human priors, such as consistency across image transformations. Given an interpretation algorithm, e.g., Grad-CAM, we introduce a novel training method to train the model to produce more consistent explanations. Since obtaining the ground truth for a desired model interpretation is not a well-defined task, we adopt ideas from contrastive self-supervised learning, and apply them to the interpretations of the model rather than its embeddings. We show that our method, Contrastive Grad-CAM Consistency (CGC), results in Grad-CAM interpretation heatmaps that are more consistent with human annotations while still achieving comparable classification accuracy. Moreover, our method acts as a regularizer and improves the accuracy on limited-data, fine-grained classification settings. In addition, because our method does not rely on annotations, it allows for the incorporation of unlabeled data into training, which enables better generalization of the model. The code is available here: https://github.com/UCDvision/CGC | https://openaccess.thecvf.com/content/CVPR2022/papers/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pillai_Consistent_Explanations_by_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2110.00527 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pillai_Consistent_Explanations_by_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Text2Pos: Text-to-Point-Cloud Cross-Modal Localization | Manuel Kolmet, Qunjie Zhou, Aljoša Ošep, Laura Leal-Taixé | Natural language-based communication with mobile devices and home appliances is becoming increasingly popular and has the potential to become natural for communicating with mobile robots in the future. Towards this goal, we investigate cross-modal text-to-point-cloud localization that will allow us to specify, for example, a vehicle pick-up or goods delivery location. In particular, we propose Text2Pos, a cross-modal localization module that learns to align textual descriptions with localization cues in a coarse- to-fine manner. Given a point cloud of the environment, Text2Pos locates a position that is specified via a natural language-based description of the immediate surroundings. To train Text2Pos and study its performance, we construct KITTI360Pose, the first dataset for this task based on the recently introduced KITTI360 dataset. Our experiments show that we can localize 65% of textual queries within 15m distance to query locations for top-10 retrieved locations. This is a starting point that we hope will spark future developments towards language-based navigation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kolmet_Text2Pos_Text-to-Point-Cloud_Cross-Modal_Localization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kolmet_Text2Pos_Text-to-Point-Cloud_Cross-Modal_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kolmet_Text2Pos_Text-to-Point-Cloud_Cross-Modal_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kolmet_Text2Pos_Text-to-Point-Cloud_Cross-Modal_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
MulT: An End-to-End Multitask Learning Transformer | Deblina Bhattacharjee, Tong Zhang, Sabine Süsstrunk, Mathieu Salzmann | We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. We will make our code and models publicly available upon publication. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bhattacharjee_MulT_An_End-to-End_Multitask_Learning_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bhattacharjee_MulT_An_End-to-End_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bhattacharjee_MulT_An_End-to-End_Multitask_Learning_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bhattacharjee_MulT_An_End-to-End_Multitask_Learning_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Hierarchical Modular Network for Video Captioning | Hanhua Ye, Guorong Li, Yuankai Qi, Shuhui Wang, Qingming Huang, Ming-Hsuan Yang | Video captioning aims to generate natural language descriptions according to the content, where representation learning plays a crucial role. Existing methods are mainly developed within the supervised learning framework via word-by-word comparison of the generated caption against the ground-truth text without fully exploiting linguistic semantics. In this work, we propose a hierarchical modular network to bridge video representations and linguistic semantics from three levels before generating captions. In particular, the hierarchy is composed of: (I) Entity level, which highlights objects that are most likely to be mentioned in captions. (II) Predicate level, which learns the actions conditioned on highlighted objects and is supervised by the predicate in captions. (III) Sentence level, which learns the global semantic representation and is supervised by the whole caption. Each level is implemented by one module. Extensive experimental results show that the proposed method performs favorably against the state-of-the-art models on the two widely-used benchmarks: MSVD 104.0% and MSR-VTT 51.5% in CIDEr score. Code will be made available at https://github.com/MarcusNerva/HMN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_Hierarchical_Modular_Network_for_Video_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_Hierarchical_Modular_Network_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12476 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Hierarchical_Modular_Network_for_Video_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_Hierarchical_Modular_Network_for_Video_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
Learning With Neighbor Consistency for Noisy Labels | Ahmet Iscen, Jack Valmadre, Anurag Arnab, Cordelia Schmid | Recent advances in deep learning have relied on large, labelled datasets to train high-capacity models. However, collecting large datasets in a time- and cost-efficient manner often results in label noise. We present a method for learning from noisy labels that leverages similarities between training examples in feature space, encouraging the prediction of each example to be similar to its nearest neighbours. Compared to training algorithms that use multiple models or distinct stages, our approach takes the form of a simple, additional regularization term. It can be interpreted as an inductive version of the classical, transductive label propagation algorithm. We thoroughly evaluate our method on datasets evaluating both synthetic (CIFAR-10, CIFAR-100) and realistic (mini-WebVision, WebVision, Clothing1M, mini-ImageNet-Red) noise, and achieve competitive or state-of-the-art accuracies across all of them. | https://openaccess.thecvf.com/content/CVPR2022/papers/Iscen_Learning_With_Neighbor_Consistency_for_Noisy_Labels_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Iscen_Learning_With_Neighbor_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.02200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Iscen_Learning_With_Neighbor_Consistency_for_Noisy_Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Iscen_Learning_With_Neighbor_Consistency_for_Noisy_Labels_CVPR_2022_paper.html | CVPR 2022 | null |
Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light | Yuhua Xu, Xiaoli Yang, Yushan Yu, Wei Jia, Zhaobi Chu, Yulan Guo | It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsystem can provide external guidance for the stereo matching networks, which can improve the matching accuracy significantly. In order to verify the effectiveness of the proposed system, we built a prototype and collected a test dataset in indoor scenes. The evaluation results show that the Bad 2.0 error of the proposed system is 28.2% of the passive stereo system when the network RAFT is used. The dataset and trained models are available at https://github.com/YuhuaXu/MonoStereoFusion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Depth_Estimation_by_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Depth_Estimation_by_Combining_Binocular_Stereo_and_Monocular_Structured-Light_CVPR_2022_paper.html | CVPR 2022 | null |
Salient-to-Broad Transition for Video Person Re-Identification | Shutao Bai, Bingpeng Ma, Hong Chang, Rui Huang, Xilin Chen | Due to the limited utilization of temporal relations in video re-id, the frame-level attention regions of mainstream methods are partial and highly similar. To address this problem, we propose a Salient-to-Broad Module (SBM) to enlarge the attention regions gradually. Specifically, in SBM, while the previous frames have focused on the most salient regions, the later frames tend to focus on broader regions. In this way, the additional information in broad regions can supplement salient regions, incurring more powerful video-level representations. To further improve SBM, an Integration-and-Distribution Module (IDM) is introduced to enhance frame-level representations. IDM first integrates features from the entire feature space and then distributes the integrated features to each spatial location. SBM and IDM are mutually beneficial since they enhance the representations from video-level and framelevel, respectively. Extensive experiments on four prevalent benchmarks demonstrate the effectiveness and superiority of our method. The source code is available at https://github.com/baist/SINet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_Salient-to-Broad_Transition_for_Video_Person_Re-Identification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bai_Salient-to-Broad_Transition_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Salient-to-Broad_Transition_for_Video_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Salient-to-Broad_Transition_for_Video_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Object-Region Video Transformers | Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson | Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, although objects can be essential for recognizing actions. In this work, we present Object-Region Video Transformers (ORViT), an object-centric approach that extends video transformer layers with a block that directly incorporates object representations. The key idea is to fuse object-centric representations starting from early layers and propagate them into the transformer-layers, thus affecting the spatio-temporal representations throughout the network. Our ORViT block consists of two object-level streams: appearance and dynamics. In the appearance stream, an "Object-Region Attention" module applies self-attention over the patches and object regions. In this way, visual object regions interact with uniform patch tokens and enrich them with contextualized object information. We further model object dynamics via a separate "Object-Dynamics Module", which captures trajectory interactions, and show how to integrate the two streams. We evaluate our model on four tasks and five datasets: compositional and few-shot action recognition on SomethingElse, spatio-temporal action detection on AVA, and standard action recognition on Something-Something V2, Diving48 and Epic-Kitchen100. We show strong performance improvement across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture. | https://openaccess.thecvf.com/content/CVPR2022/papers/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Herzig_Object-Region_Video_Transformers_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Herzig_Object-Region_Video_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
DeeCap: Dynamic Early Exiting for Efficient Image Captioning | Zhengcong Fei, Xu Yan, Shuhui Wang, Qi Tian | Both accuracy and efficiency are crucial for image captioning in real-world scenarios. Although Transformer-based models have gained significant improved captioning performance, their computational cost is very high. A feasible way to reduce the time complexity is to exit the prediction early in internal decoding layers without passing the entire model. However, it is not straightforward to devise early exiting into image captioning due to the following issues. On one hand, the representation in shallow layers lacks high-level semantic and sufficient cross-modal fusion information for accurate prediction. On the other hand, the exiting decisions made by internal classifiers are unreliable sometimes. To solve these issues, we propose DeeCap framework for efficient image captioning, which dynamically selects proper-sized decoding layers from a global perspective to exit early. The key to successful early exiting lies in the specially designed imitation learning mechanism, which predicts the deep layer activation with shallow layer features. By deliberately merging the imitation learning into the whole image captioning architecture, the imitated deep layer representation can mitigate the loss brought by the missing of actual deep layers when early exiting is undertaken, resulting in significant reduction in calculation cost with small sacrifice of accuracy. Experiments on the MS COCO and Flickr30k datasets demonstrate the DeeCap can achieve competitive performances with 4 speed-up. Code is available at: https://github.com/feizc/DeeCap. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fei_DeeCap_Dynamic_Early_Exiting_for_Efficient_Image_Captioning_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fei_DeeCap_Dynamic_Early_Exiting_for_Efficient_Image_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fei_DeeCap_Dynamic_Early_Exiting_for_Efficient_Image_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization | Nuo Xu, Jianlong Chang, Xing Nie, Chunlei Huo, Shiming Xiang, Chunhong Pan | Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation. To solve these two difficulties, an efficient parallel hyper-parameter optimization model is proposed under the framework of Deep Reinforcement Learning (DRL). Technically, we develop Attention and Memory Enhancement (AME), that includes multi-head attention and memory mechanism to enhance the ability to capture both the short-term and long-term relationships between different hyper-parameter configurations, yielding an attentive sampling mechanism for searching high-performance configurations embedded into a huge search space. During the optimization of transformer-structured configuration searcher, a conceptually intuitive yet powerful strategy is applied to solve the problem of insufficient number of samples due to the untimely feedback. Experiments on three visual tasks, including image classification, object detection, semantic segmentation, demonstrate the effectiveness of AME. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_AME_Attention_and_Memory_Enhancement_in_Hyper-Parameter_Optimization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_AME_Attention_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_AME_Attention_and_Memory_Enhancement_in_Hyper-Parameter_Optimization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_AME_Attention_and_Memory_Enhancement_in_Hyper-Parameter_Optimization_CVPR_2022_paper.html | CVPR 2022 | null |
Alignment-Uniformity Aware Representation Learning for Zero-Shot Video Classification | Shi Pu, Kaili Zhao, Mao Zheng | Most methods tackle zero-shot video classification by aligning visual-semantic representations within seen classes, which limits generalization to unseen classes. To enhance model generalizability, this paper presents an end-to-end framework that preserves alignment and uniformity properties for representations on both seen and unseen classes. Specifically, we formulate a supervised contrastive loss to simultaneously align visual-semantic features (i.e., alignment) and encourage the learned features to distribute uniformly (i.e., uniformity). Unlike existing methods that only consider the alignment, we propose uniformity to preserve maximal-info of existing features, which improves the probability that unobserved features fall around observed data. Further, we synthesize features of unseen classes by proposing a class generator that interpolates and extrapolates the features of seen classes. Besides, we introduce two metrics, closeness and dispersion, to quantify the two properties and serve as new measurements of model generalizability. Experiments show that our method significantly outperforms SoTA by relative improvements of 28.1% on UCF101 and 27.0% on HMDB51. Code is available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pu_Alignment-Uniformity_Aware_Representation_Learning_for_Zero-Shot_Video_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pu_Alignment-Uniformity_Aware_Representation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15381 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pu_Alignment-Uniformity_Aware_Representation_Learning_for_Zero-Shot_Video_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pu_Alignment-Uniformity_Aware_Representation_Learning_for_Zero-Shot_Video_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
RepMLPNet: Hierarchical Vision MLP With Re-Parameterized Locality | Xiaohan Ding, Honghao Chen, Xiangyu Zhang, Jungong Han, Guiguang Ding | Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition. In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel. Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters. Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet. The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that 1) Locality Injection is a general methodology for MLP models; 2) RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs; 3) RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation. The code and models are available at https://github.com/DingXiaoH/RepMLP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_RepMLPNet_Hierarchical_Vision_MLP_With_Re-Parameterized_Locality_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_RepMLPNet_Hierarchical_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.11081 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_RepMLPNet_Hierarchical_Vision_MLP_With_Re-Parameterized_Locality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_RepMLPNet_Hierarchical_Vision_MLP_With_Re-Parameterized_Locality_CVPR_2022_paper.html | CVPR 2022 | null |
DR.VIC: Decomposition and Reasoning for Video Individual Counting | Tao Han, Lei Bai, Junyu Gao, Qi Wang, Wanli Ouyang | Pedestrian counting is a fundamental tool for understanding pedestrian patterns and crowd flow analysis. Existing works (e.g., image-level pedestrian counting, crossline crowd counting et al.) either only focus on the image-level counting or are constrained to the manual annotation of lines. In this work, we propose to conduct the pedestrian counting from a new perspective - Video Individual Counting (VIC), which counts the total number of individual pedestrians in the given video (a person is only counted once). Instead of relying on the Multiple Object Tracking (MOT) techniques, we propose to solve the problem by decomposing all pedestrians into the initial pedestrians who existed in the first frame and the new pedestrians with separate identities in each following frame. Then, an end-to-end Decomposition and Reasoning Network (DRNet) is designed to predict the initial pedestrian count with the density estimation method and reason the new pedestrian's count of each frame with the differentiable optimal transport. Extensive experiments are conducted on two datasets with congested pedestrians and diverse scenes, demonstrating the effectiveness of our method over baselines with great superiority in counting the individual pedestrians. Code: https://github.com/taohan10200/DRNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_DR.VIC_Decomposition_and_Reasoning_for_Video_Individual_Counting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_DR.VIC_Decomposition_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_DR.VIC_Decomposition_and_Reasoning_for_Video_Individual_Counting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_DR.VIC_Decomposition_and_Reasoning_for_Video_Individual_Counting_CVPR_2022_paper.html | CVPR 2022 | null |
LiDARCap: Long-Range Marker-Less 3D Human Motion Capture With LiDAR Point Clouds | Jialian Li, Jingyi Zhang, Zhiyong Wang, Siqi Shen, Chenglu Wen, Yuexin Ma, Lan Xu, Jingyi Yu, Cheng Wang | Existing motion capture datasets are largely short-range and cannot yet fit the need of long-range applications. We propose LiDARHuman26M, a new human motion capture dataset captured by LiDAR at a much longer range to overcome this limitation. Our dataset also includes the ground truth human motions acquired by the IMU system and the synchronous RGB images. We further present a strong baseline method, LiDARCap, for LiDAR point cloud human motion capture. Specifically, we first utilize PointNet++ to encode features of points and then employ the inverse kinematics solver and SMPL optimizer to regress the pose through aggregating the temporally encoded features hierarchically. Quantitative and qualitative experiments show that our method outperforms the techniques based only on RGB images. Ablation experiments demonstrate that our dataset is challenging and worthy of further research. Finally, the experiments on the KITTI Dataset and the Waymo Open Dataset show that our method can be generalized to different LiDAR sensor settings. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_LiDARCap_Long-Range_Marker-Less_3D_Human_Motion_Capture_With_LiDAR_Point_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_LiDARCap_Long-Range_Marker-Less_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.14698 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_LiDARCap_Long-Range_Marker-Less_3D_Human_Motion_Capture_With_LiDAR_Point_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_LiDARCap_Long-Range_Marker-Less_3D_Human_Motion_Capture_With_LiDAR_Point_CVPR_2022_paper.html | CVPR 2022 | null |
GeoEngine: A Platform for Production-Ready Geospatial Research | Sagar Verma, Siddharth Gupta, Hal Shin, Akash Panigrahi, Shubham Goswami, Shweta Pardeshi, Natanael Exe, Ujwal Dutta, Tanka Raj Joshi, Nitin Bhojwani | Geospatial machine learning has seen tremendous academic advancement, but its practical application has been constrained by difficulties with operationalizing performant and reliable solutions. Sourcing satellite imagery in real-world settings, handling terabytes of training data, and managing machine learning artifacts are a few of the challenges that have severely limited downstream innovation. In this paper we introduce the GeoEngine platform for reproducible and production-ready geospatial machine learning research. GeoEngine removes key technical hurdles to adopting computer vision and deep learning-based geospatial solutions at scale. It is the first end-to-end geospatial machine learning platform, simplifying access to insights locked behind petabytes of imagery. Backed by a rigorous research methodology, this geospatial framework empowers researchers with powerful abstractions for image sourcing, dataset development, model development, large scale training, and model deployment. In this paper we provide the GeoEngine architecture explaining our design rationale in detail. We provide several real-world use cases of image sourcing, dataset development, and model building that have helped different organisations build and deploy geospatial solutions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Verma_GeoEngine_A_Platform_for_Production-Ready_Geospatial_Research_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Verma_GeoEngine_A_Platform_for_Production-Ready_Geospatial_Research_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Verma_GeoEngine_A_Platform_for_Production-Ready_Geospatial_Research_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting Document Image Dewarping by Grid Regularization | Xiangwei Jiang, Rujiao Long, Nan Xue, Zhibo Yang, Cong Yao, Gui-Song Xia | This paper addresses the problem of document image dewarping, which aims at eliminating the geometric distortion in document images for document digitization. Instead of designing a better neural network to approximate the optical flow fields between the inputs and outputs, we pursue the best readability by taking the text lines and the document boundaries into account from a constrained optimization perspective. Specifically, our proposed method first learns the boundary points and the pixels in the text lines and then follows the most simple observation that the boundaries and text lines in both horizontal and vertical directions should be kept after dewarping to introduce a novel grid regularization scheme. To obtain the final forward mapping for dewarping, we solve an optimization problem with our proposed grid regularization. The experiments comprehensively demonstrate that our proposed approach outperforms the prior arts by large margins in terms of readability (with the metrics of Character Errors Rate and the Edit Distance) while maintaining the best image quality on the publicly-available DocUNet benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Revisiting_Document_Image_Dewarping_by_Grid_Regularization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Revisiting_Document_Image_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16850 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Revisiting_Document_Image_Dewarping_by_Grid_Regularization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Revisiting_Document_Image_Dewarping_by_Grid_Regularization_CVPR_2022_paper.html | CVPR 2022 | null |
Semi-Supervised Few-Shot Learning via Multi-Factor Clustering | Jie Ling, Lei Liao, Meng Yang, Jia Shuai | The scarcity of labeled data and the problem of model overfitting have been the challenges in few-shot learning. Recently, semi-supervised few-shot learning has been developed to obtain pseudo-labels of unlabeled samples for expanding the support set. However, the relationship between unlabeled and labeled data is not well exploited in generating pseudo labels, the noise of which will directly harm the model learning. In this paper, we propose a Clustering-based semi-supervised Few-Shot Learning (cluster-FSL) method to solve the above problems in image classification. By using multi-factor collaborative representation, a novel Multi-Factor Clustering (MFC) is designed to fuse the information of few-shot data distribution, which can generate soft and hard pseudo-labels for unlabeled samples based on labeled data. And we exploit the pseudo labels of unlabeled samples by MFC to expand the support set for obtaining more distribution information. Furthermore, robust data augmentation is used for support set in fine-tuning phase to increase the diversity of labeled samples. We verified the validity of the cluster-FSL by comparing it with other few-shot learning methods on three popular benchmark datasets, miniImageNet, tieredImageNet, and CUB-200-2011. The ablation experiments further demonstrate that our MFC can effectively fuse distribution information of labeled samples and provide high-quality pseudo-labels. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ling_Semi-Supervised_Few-Shot_Learning_via_Multi-Factor_Clustering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ling_Semi-Supervised_Few-Shot_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ling_Semi-Supervised_Few-Shot_Learning_via_Multi-Factor_Clustering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ling_Semi-Supervised_Few-Shot_Learning_via_Multi-Factor_Clustering_CVPR_2022_paper.html | CVPR 2022 | null |
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation | Qihang Yu, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen | We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering. It rethinks the existing transformer architectures used in segmentation and detection; CMT-DeepLab considers the object queries as cluster centers, which fill the role of grouping the pixels when applied to segmentation. The clustering is computed with an alternating procedure, by first assigning pixels to the clusters by their feature affinity, and then updating the cluster centers and pixel features. Together, these operations comprise the Clustering Mask Transformer (CMT) layer, which produces cross-attention that is denser and more consistent with the final segmentation task. CMT-DeepLab improves the performance over prior art significantly by 4.4% PQ, achieving a new state-of-the-art of 55.7% PQ on the COCO test-dev set. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_CMT-DeepLab_Clustering_Mask_Transformers_for_Panoptic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_CMT-DeepLab_Clustering_Mask_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_CMT-DeepLab_Clustering_Mask_Transformers_for_Panoptic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_CMT-DeepLab_Clustering_Mask_Transformers_for_Panoptic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly-Supervised Generation and Grounding of Visual Descriptions With Conditional Generative Models | Effrosyni Mavroudi, René Vidal | Given weak supervision from image- or video-caption pairs, we address the problem of grounding (localizing) each object word of a ground-truth or generated sentence describing a visual input. Recent weakly-supervised approaches leverage region proposals and ground words based on the region attention coefficients of captioning models. To predict each next word in the sentence they attend over regions using a summary of the previous words as a query, and then ground the word by selecting the most attended regions. However, this leads to sub-optimal grounding, since attention coefficients are computed without taking into account the word that needs to be localized. To address this shortcoming, we propose a novel Grounded Visual Description Conditional Variational Autoencoder (GVD-CVAE) and leverage its latent variables for grounding. In particular, we introduce a discrete random variable that models each word-to-region alignment, and learn its approximate posterior distribution given the full sentence. Experiments on challenging image and video datasets (Flickr30k Entities, YouCook2, ActivityNet Entities) validate the effectiveness of our conditional generative model, showing that it can substantially outperform soft-attention-based baselines in grounding. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mavroudi_Weakly-Supervised_Generation_and_Grounding_of_Visual_Descriptions_With_Conditional_Generative_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mavroudi_Weakly-Supervised_Generation_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mavroudi_Weakly-Supervised_Generation_and_Grounding_of_Visual_Descriptions_With_Conditional_Generative_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mavroudi_Weakly-Supervised_Generation_and_Grounding_of_Visual_Descriptions_With_Conditional_Generative_CVPR_2022_paper.html | CVPR 2022 | null |
Novel Class Discovery in Semantic Segmentation | Yuyang Zhao, Zhun Zhong, Nicu Sebe, Gim Hee Lee | We introduce a new setting of Novel Class Discovery in Semantic Segmentation (NCDSS), which aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes. In contrast to existing approaches that look at novel class discovery in image classification, we focus on the more challenging semantic segmentation. In NCDSS, we need to distinguish the objects and background, and to handle the existence of multiple classes within an image, which increases the difficulty in using the unlabeled data. To tackle this new setting, we leverage the labeled base data and a saliency model to coarsely cluster novel classes for model training in our basic framework. Additionally, we propose the Entropy-based Uncertainty Modeling and Self-training (EUMS) framework to overcome noisy pseudo-labels, further improving the model performance on the novel classes. Our EUMS utilizes an entropy ranking technique and a dynamic reassignment to distill clean labels, thereby making full use of the noisy data via self-supervised learning. We build the NCDSS benchmark on the PASCAL-5^i dataset and COCO-20^i dataset. Extensive experiments demonstrate the feasibility of the basic framework (achieving an average mIoU of 49.81% on PASCAL-5^i) and the effectiveness of EUMS framework (outperforming the basic framework by 9.28% mIoU on PASCAL-5^i). | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Novel_Class_Discovery_in_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Novel_Class_Discovery_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01900 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Novel_Class_Discovery_in_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Novel_Class_Discovery_in_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
ARCS: Accurate Rotation and Correspondence Search | Liangzu Peng, Manolis C. Tsakiris, René Vidal | This paper is about the old Wahba problem in its more general form, which we call "simultaneous rotation and correspondence search". In this generalization we need to find a rotation that best aligns two partially overlapping 3D point sets, of sizes m and n respectively with m\geq n. We first propose a solver, \texttt ARCS , that i) assumes noiseless point sets in general position, ii) requires only 2 inliers, iii) uses O(m\log m) time and O(m) space, and iv) can successfully solve the problem even with, e.g., m,n~ 10^6 in about 0.1 seconds. We next robustify \texttt ARCS to noise, for which we approximately solve consensus maximization problems using ideas from robust subspace learning and interval stabbing. Thirdly, we refine the approximately found consensus set by a Riemannian subgradient descent approach over the space of unit quaternions, which we show converges globally to an \varepsilon-stationary point in O(\varepsilon^ -4 ) iterations, or locally to the ground-truth at a linear rate in the absence of noise. We combine these algorithms into \texttt ARCS+ , to simultaneously search for rotations and correspondences. Experiments show that \texttt ARCS+ achieves state-of-the-art performance on large-scale datasets with more than 10^6 points with a 10^4 time-speedup over alternative methods. https://github.com/liangzu/ARCS | https://openaccess.thecvf.com/content/CVPR2022/papers/Peng_ARCS_Accurate_Rotation_and_Correspondence_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Peng_ARCS_Accurate_Rotation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_ARCS_Accurate_Rotation_and_Correspondence_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Peng_ARCS_Accurate_Rotation_and_Correspondence_Search_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Anticipate Future With Dynamic Context Removal | Xinyu Xu, Yong-Lu Li, Cewu Lu | Anticipating future events is an essential feature for intelligent systems and embodied AI. However, compared to the traditional recognition task, the uncertainty of future and reasoning ability requirement make the anticipation task very challenging and far beyond solved. In this filed, previous methods usually care more about the model architecture design or but few attention has been put on how to train an anticipation model with a proper learning policy. To this end, in this work, we propose a novel training scheme called Dynamic Context Removal (DCR), which dynamically schedule the visibility of observed future in the learning procedure. It follows the human-like curriculum learning process, i.e., gradually removing the event context to increase the anticipation difficulty till satisfying the final anticipation target. Our learning scheme is plug-and-play and easy to integrate any reasoning model including transformer and LSTM, with advantages in both effectiveness and efficiency. In extensive experiments, the proposed method achieves state-of-the-art on four widely-used benchmarks. Our code and models are publicly released at https://github.com/AllenXuuu/DCR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Learning_To_Anticipate_Future_With_Dynamic_Context_Removal_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Learning_To_Anticipate_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02587 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Learning_To_Anticipate_Future_With_Dynamic_Context_Removal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Learning_To_Anticipate_Future_With_Dynamic_Context_Removal_CVPR_2022_paper.html | CVPR 2022 | null |
GCFSR: A Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors | Jingwen He, Wu Shi, Kai Chen, Lean Fu, Chao Dong | Face image super resolution (face hallucination) usually relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impressive results with the help of GAN prior. They either design complicated modules to modify the fixed GAN prior or adopt complex training strategies to finetune the generator. In this work, we propose a generative and controllable face SR framework, called GCFSR, which can reconstruct images with faithful identity information without any additional priors. Generally, GCFSR has an encoder-generator architecture. Two modules called style modulation and feature modulation are designed for the multi-factor SR task. The style modulation aims to generate realistic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor. The simple and elegant architecture can be trained from scratch in an end-to-end manner. For small upscaling factors (\leq8), GCFSR can produce surprisingly good results with only adversarial loss. After adding L1 and perceptual losses, GCFSR can outperform state-of-the-art methods for large upscaling factors (16, 32, 64). During the test phase, we can modulate the generative strength via feature modulation by changing the conditional upscaling factor continuously to achieve various generative effects. Code is available at https: //github.com/hejingwenhejingwen/GCFSR. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_GCFSR_A_Generative_and_Controllable_Face_Super_Resolution_Method_Without_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_GCFSR_A_Generative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07319 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_GCFSR_A_Generative_and_Controllable_Face_Super_Resolution_Method_Without_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_GCFSR_A_Generative_and_Controllable_Face_Super_Resolution_Method_Without_CVPR_2022_paper.html | CVPR 2022 | null |
Perception Prioritized Training of Diffusion Models | Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon | Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising score matching loss. In this paper, we show that restoring data corrupted with certain noise levels offers a proper pretext task for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function. We show that our simple redesign of the weighting scheme significantly improves the performance of diffusion models regardless of the datasets, architectures, and sampling strategies. | https://openaccess.thecvf.com/content/CVPR2022/papers/Choi_Perception_Prioritized_Training_of_Diffusion_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Choi_Perception_Prioritized_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Perception_Prioritized_Training_of_Diffusion_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Perception_Prioritized_Training_of_Diffusion_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Using 3D Topological Connectivity for Ghost Particle Reduction in Flow Reconstruction | Christina Tsalicoglou, Thomas Rösgen | Volumetric flow velocimetry for experimental fluid dynamics relies primarily on the 3D reconstruction of point objects, which are the detected positions of tracer particles identified in images obtained by a multi-camera setup. By assuming that the particles accurately follow the observed flow, their displacement over a known time interval is a measure of the local flow velocity. The number of particles imaged in a 1 Megapixel image is typically in the order of 1e3-1e4, resulting in a large number of consistent but incorrect reconstructions (no real particle in 3D), that must be eliminated through tracking or intensity constraints. In an alternative method, 3D Particle Streak Velocimetry (3D-PSV), the exposure time is increased, and the particles' pathlines are imaged as "streaks". We treat these streaks (a) as connected endpoints and (b) as conic section segments and develop a theoretical model that describes the mechanisms of 3D ambiguity generation and shows that streaks can drastically reduce reconstruction ambiguities. Moreover, we propose a method for simultaneously estimating these short, low-curvature conic section segments and their 3D position from multiple camera views. Our results validate the theory, and the streak and conic section reconstruction method produces far fewer ambiguities than simple particle reconstruction, outperforming current state-of-the-art particle tracking software on the evaluated cases. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tsalicoglou_Using_3D_Topological_Connectivity_for_Ghost_Particle_Reduction_in_Flow_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tsalicoglou_Using_3D_Topological_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tsalicoglou_Using_3D_Topological_Connectivity_for_Ghost_Particle_Reduction_in_Flow_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tsalicoglou_Using_3D_Topological_Connectivity_for_Ghost_Particle_Reduction_in_Flow_CVPR_2022_paper.html | CVPR 2022 | null |
On the Integration of Self-Attention and Convolution | Xuran Pan, Chunjiang Ge, Rui Lu, Shiji Song, Guanfu Chen, Zeyi Huang, Gao Huang | Convolution and self-attention are two powerful techniques for representation learning, and they are usually considered as two peer approaches that are distinct from each other. In this paper, we show that there exists a strong underlying relation between them, in the sense that the bulk of computations of these two paradigms are in fact done with the same operation. Specifically, we first show that a traditional convolution with kernel size k x k can be decomposed into k^2 individual 1x1 convolutions, followed by shift and summation operations. Then, we interpret the projections of queries, keys, and values in selfattention module as multiple 1x1 convolutions, followed by the computation of attention weights and aggregation of the values. Therefore, the first stage of both two modules comprises the similar operation. More importantly, the first stage contributes a dominant computation complexity (square of the channel size) comparing to the second stage. This observation naturally leads to an elegant integration of these two seemingly distinct paradigms, i.e., a mixed model that enjoys the benefit of both self-Attention and Convolution (ACmix), while having minimum computational overhead compared to the pure convolution or selfattention counterpart. Extensive experiments show that our model achieves consistently improved results over competitive baselines on image recognition and downstream tasks. Code and pre-trained models will be released at https://github.com/LeapLabTHU/ACmix and https://gitee.com/mindspore/models. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pan_On_the_Integration_of_Self-Attention_and_Convolution_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pan_On_the_Integration_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14556 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_On_the_Integration_of_Self-Attention_and_Convolution_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pan_On_the_Integration_of_Self-Attention_and_Convolution_CVPR_2022_paper.html | CVPR 2022 | null |
Progressively Generating Better Initial Guesses Towards Next Stages for High-Quality Human Motion Prediction | Tiezheng Ma, Yongwei Nie, Chengjiang Long, Qing Zhang, Guiqing Li | This paper presents a high-quality human motion prediction method that accurately predicts future human poses given observed ones. Our method is based on the observation that a good initial guess of the future poses is very helpful in improving the forecasting accuracy. This motivates us to propose a novel two-stage prediction framework, including an init-prediction network that just computes the good guess and then a formal-prediction network that predicts the target future poses based on the guess. More importantly, we extend this idea further and design a multi-stage prediction framework where each stage predicts initial guess for the next stage, which brings more performance gain. To fulfill the prediction task at each stage, we propose a network comprising Spatial Dense Graph Convolutional Networks (S-DGCN) and Temporal Dense Graph Convolutional Networks (T-DGCN). Alternatively executing the two networks helps extract spatiotemporal features over the global receptive field of the whole pose sequence. All the above design choices cooperating together make our method outperform previous approaches by large margins: 6%-7% on Human3.6M, 5%-10% on CMU-MoCap, and 13%-16% on 3DPW. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ma_Progressively_Generating_Better_Initial_Guesses_Towards_Next_Stages_for_High-Quality_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Progressively_Generating_Better_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16051 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Progressively_Generating_Better_Initial_Guesses_Towards_Next_Stages_for_High-Quality_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Progressively_Generating_Better_Initial_Guesses_Towards_Next_Stages_for_High-Quality_CVPR_2022_paper.html | CVPR 2022 | null |
CHEX: CHannel EXploration for CNN Model Compression | Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung | Channel pruning has been broadly recognized as an effective technique to reduce the computation and memory cost of deep convolutional neural networks. However, conventional pruning methods have limitations in that: they are restricted to pruning process only, and they require a fully pre-trained large model. Such limitations may lead to sub-optimal model quality as well as excessive memory and training cost. In this paper, we propose a novel Channel Exploration methodology, dubbed as CHEX, to rectify these problems. As opposed to pruning-only strategy, we propose to repeatedly prune and regrow the channels throughout the training process, which reduces the risk of pruning important channels prematurely. More exactly: From intra-layer's aspect, we tackle the channel pruning problem via a well-known column subset selection (CSS) formulation. From inter-layer's aspect, our regrowing stages open a path for dynamically re-allocating the number of channels across all the layers under a global channel sparsity constraint. In addition, all the exploration process is done in a single training from scratch without the need of a pre-trained large model. Experimental results demonstrate that CHEX can effectively reduce the FLOPs of diverse CNN architectures on a variety of computer vision tasks, including image classification, object detection, instance segmentation, and 3D vision. For example, our compressed ResNet-50 model on ImageNet dataset achieves 76% top-1 accuracy with only 25% FLOPs of the original ResNet-50 model, outperforming previous state-of-the-art channel pruning methods. The checkpoints and code are available at here. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hou_CHEX_CHannel_EXploration_for_CNN_Model_Compression_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hou_CHEX_CHannel_EXploration_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15794 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hou_CHEX_CHannel_EXploration_for_CNN_Model_Compression_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hou_CHEX_CHannel_EXploration_for_CNN_Model_Compression_CVPR_2022_paper.html | CVPR 2022 | null |
M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction | Qiao Sun, Xin Huang, Junru Gu, Brian C. Williams, Hang Zhao | Predicting future motions of road participants is an important task for driving autonomously in urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet it remains an open question to jointly predict scene compliant trajectories over multiple agents. The challenge is due to exponentially increasing prediction space as a function of the number of agents. In this work, we exploit the underlying relations between interacting agents and decouple the joint prediction problem into marginal prediction problems. Our proposed approach M2I first classifies interacting agents as pairs of influencers and reactors, and then leverages a marginal prediction model and a conditional prediction model to predict trajectories for the influencers and reactors, respectively. The predictions from interacting agents are combined and selected according to their joint likelihoods. Experiments show that our simple but effective approach achieves state-of-the-art performance on the Waymo Open Motion Dataset interactive prediction benchmark. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_M2I_From_Factored_Marginal_Trajectory_Prediction_to_Interactive_Prediction_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2202.11884 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_M2I_From_Factored_Marginal_Trajectory_Prediction_to_Interactive_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_M2I_From_Factored_Marginal_Trajectory_Prediction_to_Interactive_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Domain Adaptation on Point Clouds via Geometry-Aware Implicits | Yuefan Shen, Yanchao Yang, Mi Yan, He Wang, Youyi Zheng, Leonidas J. Guibas | As a popular geometric representation, point clouds have attracted much attention in 3D vision, leading to many applications in autonomous driving and robotics. One important yet unsolved issue for learning on point cloud is that point clouds of the same object can have significant geometric variations if generated using different procedures or captured using different sensors. These inconsistencies induce domain gaps such that neural networks trained on one domain may fail to generalize on others. A typical technique to reduce the domain gap is to perform adversarial training so that point clouds in the feature space can align. However, adversarial training is easy to fall into degenerated local minima, resulting in negative adaptation gains. Here we propose a simple yet effective method for unsupervised domain adaptation on point clouds by employing a self-supervised task of learning geometry-aware implicits, which plays two critical roles in one shot. First, the geometric information in the point clouds is preserved through the implicit representations for downstream tasks. More importantly, the domain-specific variations can be effectively learned away in the implicit space. We also propose an adaptive strategy to compute unsigned distance fields for arbitrary point clouds due to the lack of shape models in practice. When combined with a task loss, the proposed outperforms state-of-the-art unsupervised domain adaptation methods that rely on adversarial domain alignment and more complicated self-supervised tasks. Our method is evaluated on both PointDA-10 and GraspNet datasets. Code and data are available at: https://github.com/Jhonve/ImplicitPCDA | https://openaccess.thecvf.com/content/CVPR2022/papers/Shen_Domain_Adaptation_on_Point_Clouds_via_Geometry-Aware_Implicits_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shen_Domain_Adaptation_on_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09343 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_Domain_Adaptation_on_Point_Clouds_via_Geometry-Aware_Implicits_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_Domain_Adaptation_on_Point_Clouds_via_Geometry-Aware_Implicits_CVPR_2022_paper.html | CVPR 2022 | null |
Consistency Driven Sequential Transformers Attention Model for Partially Observable Scenes | Samrudhdhi B. Rangrej, Chetan L. Srinidhi, James J. Clark | Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many applications (e.g., aerial imaging), observing an entire scene is not always feasible due to the limited time and resources available for acquisition. In this paper, we develop a Sequential Transformers Attention Model (STAM) that only partially observes a complete image and predicts informative glimpse locations solely based on past glimpses. We design our agent using DeiT-distilled and train it with a one-step actor-critic algorithm. Furthermore, to improve classification performance, we introduce a novel training objective, which enforces consistency between the class distribution predicted by a teacher model from a complete image and the class distribution predicted by our agent using glimpses. When the agent senses only 4% of the total image area, the inclusion of the proposed consistency loss in our training objective yields 3% and 8% higher accuracy on ImageNet and fMoW datasets, respectively. Moreover, our agent outperforms previous state-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses on ImageNet and fMoW. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rangrej_Consistency_Driven_Sequential_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00656 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
GroupViT: Semantic Segmentation Emerges From Text Supervision | Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang | Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. With end-to-end deep learning systems, grouping of image regions usually happens implicitly via top-down supervision from pixel-level recognition labels. Instead, in this paper, we propose to bring back the grouping mechanism into deep networks, which allows semantic segments to emerge automatically with only text supervision. We propose a hierarchical Grouping Vision Transformer (GroupViT), which goes beyond the regular grid structure representation and learns to group image regions into progressively larger arbitrary-shaped segments. We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. With only text supervision and without any pixel-level annotations, GroupViT learns to group together semantic regions and successfully transfers to the task of semantic segmentation in a zero-shot manner, i.e., without any further fine-tuning. It achieves a zero-shot accuracy of 52.3% mIoU on the PASCAL VOC 2012 and 22.4% mIoU on PASCAL Context datasets, and performs competitively to state-of-the-art transfer-learning methods requiring greater levels of supervision. We open-source our code at https://github.com/NVlabs/GroupViT | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_GroupViT_Semantic_Segmentation_Emerges_From_Text_Supervision_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_GroupViT_Semantic_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.11094 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_GroupViT_Semantic_Segmentation_Emerges_From_Text_Supervision_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_GroupViT_Semantic_Segmentation_Emerges_From_Text_Supervision_CVPR_2022_paper.html | CVPR 2022 | null |
NeuralHOFusion: Neural Volumetric Rendering Under Human-Object Interactions | Yuheng Jiang, Suyi Jiang, Guoxing Sun, Zhuo Su, Kaiwen Guo, Minye Wu, Jingyi Yu, Lan Xu | 4D modeling of human-object interactions is critical for numerous applications. However, efficient volumetric capture and rendering of complex interaction scenarios, especially from sparse inputs, remain challenging. In this paper, we propose NeuralHOFusion, a neural approach for volumetric human-object capture and rendering using sparse consumer RGBD sensors. It marries traditional non-rigid fusion with recent neural implicit modeling and blending advances, where the captured humans and objects are layer-wise disentangled. For geometry modeling, we propose a neural implicit inference scheme with non-rigid key-volume fusion, as well as a template-aid robust object tracking pipeline. Our scheme enables detailed and complete geometry generation under complex interactions and occlusions. Moreover, we introduce a layer-wise human-object texture rendering scheme, which combines volumetric and image-based rendering in both spatial and temporal domains to obtain photo-realistic results. Extensive experiments demonstrate the effectiveness and efficiency of our approach in synthesizing photo-realistic free-view results under complex human-object interactions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_NeuralHOFusion_Neural_Volumetric_Rendering_Under_Human-Object_Interactions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_NeuralHOFusion_Neural_Volumetric_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.12825 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_NeuralHOFusion_Neural_Volumetric_Rendering_Under_Human-Object_Interactions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_NeuralHOFusion_Neural_Volumetric_Rendering_Under_Human-Object_Interactions_CVPR_2022_paper.html | CVPR 2022 | null |
Generalizable Human Pose Triangulation | Kristijan Bartol, David Bojanić, Tomislav Petković, Tomislav Pribanić | We address the problem of generalizability for multi-view 3D human pose estimation. The standard approach is to first detect 2D keypoints in images and then apply triangulation from multiple views. Even though the existing methods achieve remarkably accurate 3D pose estimation on public benchmarks, most of them are limited to a single spatial camera arrangement and their number. Several methods address this limitation but demonstrate significantly degraded performance on novel views. We propose a stochastic framework for human pose triangulation and demonstrate a superior generalization across different camera arrangements on two public datasets. In addition, we apply the same approach to the fundamental matrix estimation problem, showing that the proposed method can successfully apply to other computer vision problems. The stochastic framework achieves more than 8.8% improvement on the 3D pose estimation task, compared to the state-of-the-art, and more than 30% improvement for fundamental matrix estimation, compared to a standard algorithm. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bartol_Generalizable_Human_Pose_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bartol_Generalizable_Human_Pose_Triangulation_CVPR_2022_paper.html | CVPR 2022 | null |
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation | Gwanghyun Kim, Taesung Kwon, Jong Chul Ye | Recently, GAN inversion methods combined with Contrastive Language-Image Pretraining (CLIP) enables zero-shot image manipulation guided by text prompts. However, their applications to diverse real images are still difficult due to the limited GAN inversion capability. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. To mitigate these problems and enable faithful manipulation of real images, we propose a novel method, dubbed DiffusionCLIP, that performs text-driven image manipulation using diffusion models. Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Extensive experiments and human evaluation confirmed robust and superior manipulation performance of our methods compared to the existing baselines. Code is available at https://github.com/gwang-kim/DiffusionCLIP.git | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_DiffusionCLIP_Text-Guided_Diffusion_Models_for_Robust_Image_Manipulation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_DiffusionCLIP_Text-Guided_Diffusion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.02711 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_DiffusionCLIP_Text-Guided_Diffusion_Models_for_Robust_Image_Manipulation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_DiffusionCLIP_Text-Guided_Diffusion_Models_for_Robust_Image_Manipulation_CVPR_2022_paper.html | CVPR 2022 | null |
Occlusion-Aware Cost Constructor for Light Field Depth Estimation | Yingqian Wang, Longguang Wang, Zhengyu Liang, Jungang Yang, Wei An, Yulan Guo | Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation methods construct matching cost by sequentially shifting each sub-aperture image (SAI) with a series of predefined offsets, which is complex and time-consuming. In this paper, we propose a simple and fast cost constructor to construct matching cost for LF depth estimation. Our cost constructor is composed by a series of convolutions with specifically designed dilation rates. By applying our cost constructor to SAI arrays, pixels under predefined disparities can be integrated and matching cost can be constructed without using any shifting operation. More importantly, the proposed cost constructor is occlusion-aware and can handle occlusions by dynamically modulating pixels from different views. Based on the proposed cost constructor, we develop a deep network for LF depth estimation. Our network ranks first on the commonly used 4D LF benchmark in terms of the mean square error (MSE), and achieves a faster running time than other state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Occlusion-Aware_Cost_Constructor_for_Light_Field_Depth_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Occlusion-Aware_Cost_Constructor_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01576 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Occlusion-Aware_Cost_Constructor_for_Light_Field_Depth_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Occlusion-Aware_Cost_Constructor_for_Light_Field_Depth_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
SmartPortraits: Depth Powered Handheld Smartphone Dataset of Human Portraits for State Estimation, Reconstruction and Synthesis | Anastasiia Kornilova, Marsel Faizullin, Konstantin Pakulev, Andrey Sadkov, Denis Kukushkin, Azat Akhmetyanov, Timur Akhtyamov, Hekmat Taherinejad, Gonzalo Ferrer | We present a dataset of 1000 video sequences of human portraits recorded in real and uncontrolled conditions by using a handheld smartphone accompanied by an external high-quality depth camera. The collected dataset contains 200 people captured in different poses and locations and its main purpose is to bridge the gap between raw measurements obtained from a smartphone and downstream applications, such as state estimation, 3D reconstruction, view synthesis, etc. The sensors employed in data collection are the smartphone's camera and Inertial Measurement Unit (IMU), and an external Azure Kinect DK depth camera software synchronized with sub-millisecond precision to the smartphone system. During the recording, the smartphone flash is used to provide a periodic secondary source of lightning. Accurate mask of the foremost person is provided as well as its impact on camera alignment accuracy. For evaluation purposes, we compare multiple state-of-the-art camera alignment methods by using a Motion Capture system. We provide a smartphone visual-inertial benchmark for portrait capturing, where we report results for multiple methods and motivate further use of the provided trajectories, available in the dataset, in view synthesis and 3D reconstruction tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kornilova_SmartPortraits_Depth_Powered_Handheld_Smartphone_Dataset_of_Human_Portraits_for_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.10211 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kornilova_SmartPortraits_Depth_Powered_Handheld_Smartphone_Dataset_of_Human_Portraits_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kornilova_SmartPortraits_Depth_Powered_Handheld_Smartphone_Dataset_of_Human_Portraits_for_CVPR_2022_paper.html | CVPR 2022 | null |
BppAttack: Stealthy and Efficient Trojan Attacks Against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning | Zhenting Wang, Juan Zhai, Shiqing Ma | Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerable to human inspection. In this paper, we propose stealthy and efficient Trojan attacks, BppAttack. Based on existing biology literature on human visual systems, we propose to use image quantization and dithering as the Trojan trigger, making imperceptible changes. It is a stealthy and efficient attack without training auxiliary models. Due to the small changes made to images, it is hard to inject such triggers during training. To alleviate this problem, we propose a contrastive learning based approach that leverages adversarial attacks to generate negative sample pairs so that the learned trigger is precise and accurate. The proposed method achieves high attack success rates on four benchmark datasets, including MNIST, CIFAR-10, GTSRB, and CelebA. It also effectively bypasses existing Trojan defenses and human inspection. Our code can be found in https://github.com/RU-System-Software-and-Security/BppAttack. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_BppAttack_Stealthy_and_Efficient_Trojan_Attacks_Against_Deep_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_BppAttack_Stealthy_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.13383 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_BppAttack_Stealthy_and_Efficient_Trojan_Attacks_Against_Deep_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_BppAttack_Stealthy_and_Efficient_Trojan_Attacks_Against_Deep_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
GlideNet: Global, Local and Intrinsic Based Dense Embedding NETwork for Multi-Category Attributes Prediction | Kareem Metwaly, Aerin Kim, Elliot Branson, Vishal Monga | Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and is often formulated as a multi-label classification problem. Yet significant challenges remain in: 1) predicting a large number of attributes over multiple object categories, 2) modeling category-dependence of attributes, 3) methodically capturing both global and local scene context, and 4) robustly predicting attributes of objects with low pixel-count. To address these issues, we propose a novel multi-category attribute prediction deep architecture named GlideNet, which contains three distinct feature extractors. A global feature extractor recognizes what objects are present in a scene, whereas a local one focuses on the area surrounding the object of interest. Meanwhile, an intrinsic feature extractor uses an extension of standard convolution dubbed Informed Convolution to retrieve features of objects with low pixel-count utilizing its binary mask. GlideNet then uses gating mechanisms with binary masks and its self-learned category embedding to combine the dense embeddings. Collectively, the Global-Local-Intrinsic blocks comprehend the scene's global context while attending to the characteristics of the local object of interest. The architecture adapts the feature composition based on the category via category embedding. Finally, using the combined features, an interpreter predicts the attributes, and the length of the output is determined by the category, thereby removing unnecessary attributes. GlideNet can achieve compelling results on two recent and challenging datasets -- VAW and CAR -- for large-scale attribute prediction. For instance, it obtains more than 5% gain over state of the art in the mean recall (mR) metric. GlideNet's advantages are especially apparent when predicting attributes of objects with low pixel counts as well as attributes that demand global context understanding. Finally, we show that GlideNet excels in training starved real-world scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Metwaly_GlideNet_Global_Local_and_Intrinsic_Based_Dense_Embedding_NETwork_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Metwaly_GlideNet_Global_Local_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03079 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Metwaly_GlideNet_Global_Local_and_Intrinsic_Based_Dense_Embedding_NETwork_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Metwaly_GlideNet_Global_Local_and_Intrinsic_Based_Dense_Embedding_NETwork_for_CVPR_2022_paper.html | CVPR 2022 | null |
Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation | Xingning Dong, Tian Gan, Xuemeng Song, Jianlong Wu, Yuan Cheng, Liqiang Nie | Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also fail to provide informative predicates due to the biased relationship predictions, leading SGG far from practical. Towards this end, in this paper, we first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction, to serve as the encoder. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder. Particularly, based upon the observation that the recognition capability of one classifier is limited towards an extremely unbalanced dataset, we first deploy a group of classifiers that are expert in distinguishing different subsets of classes, and then cooperatively optimize them from two aspects to promote the unbiased SGG. Experiments conducted on VG and GQA datasets demonstrate that, we not only establish a new state-of-the-art in the unbiased metric, but also nearly double the performance compared with two baselines. Our code is available at https://github.com/dongxingning/SHA-GCL-for-SGG. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Stacked_Hybrid-Attention_and_Group_Collaborative_Learning_for_Unbiased_Scene_Graph_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Stacked_Hybrid-Attention_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09811 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Stacked_Hybrid-Attention_and_Group_Collaborative_Learning_for_Unbiased_Scene_Graph_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Stacked_Hybrid-Attention_and_Group_Collaborative_Learning_for_Unbiased_Scene_Graph_CVPR_2022_paper.html | CVPR 2022 | null |
Ensembling Off-the-Shelf Models for GAN Training | Nupur Kumari, Richard Zhang, Eli Shechtman, Jun-Yan Zhu | The advent of large-scale training has produced a cornucopia of powerful visual recognition models. However, generative models, such as GANs, have traditionally been trained from scratch in an unsupervised manner. Can the collective "knowledge" from a large bank of pretrained vision models be leveraged to improve GAN training? If so, with so many models to choose from, which one(s) should be selected, and in what manner are they most effective? We find that pretrained computer vision models can significantly improve performance when used in an ensemble of discriminators. Notably, the particular subset of selected models greatly affects performance. We propose an effective selection mechanism, by probing the linear separability between real and fake samples in pretrained model embeddings, choosing the most accurate model, and progressively adding it to the discriminator ensemble. Interestingly, our method can improve GAN training in both limited data and large-scale settings. Given only 10k training samples, our FID on LSUN Cat matches the StyleGAN2 trained on 1.6M images. On the full dataset, our method improves FID by 1.5 to 2 times on cat, church, and horse categories of LSUN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kumari_Ensembling_Off-the-Shelf_Models_for_GAN_Training_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.09130 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kumari_Ensembling_Off-the-Shelf_Models_for_GAN_Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kumari_Ensembling_Off-the-Shelf_Models_for_GAN_Training_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Better Plasticity-Stability Trade-Off in Incremental Learning: A Simple Linear Connector | Guoliang Lin, Hanlu Chu, Hanjiang Lai | Plasticity-stability dilemma is a main problem for incremental learning, where plasticity is referring to the ability to learn new knowledge, and stability retains the knowledge of previous tasks. Many methods tackle this problem by storing previous samples, while in some applications, training data from previous tasks cannot be legally stored. In this work, we propose to employ mode connectivity in loss landscapes to achieve better plasticity-stability trade-off without any previous samples. We give an analysis of why and how to connect two independently optimized optima of networks, null-space projection for previous tasks and simple SGD for the current task, can attain a meaningful balance between preserving already learned knowledge and granting sufficient flexibility for learning a new task. This analysis of mode connectivity also provides us a new perspective and technology to control the trade-off between plasticity and stability. We evaluate the proposed method on several benchmark datasets. The results indicate our simple method can achieve notable improvement, and perform well on both the past and current tasks. On 10-split-CIFAR-100 task, our method achieves 79.79% accuracy, which is 6.02% higher. Our method also achieves 6.33% higher accuracy on TinyImageNet. Code is available at https://github.com/lingl1024/Connector. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_Towards_Better_Plasticity-Stability_Trade-Off_in_Incremental_Learning_A_Simple_Linear_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2110.07905 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Towards_Better_Plasticity-Stability_Trade-Off_in_Incremental_Learning_A_Simple_Linear_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_Towards_Better_Plasticity-Stability_Trade-Off_in_Incremental_Learning_A_Simple_Linear_CVPR_2022_paper.html | CVPR 2022 | null |
Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow | Shanlin Sun, Kun Han, Deying Kong, Hao Tang, Xiangyi Yan, Xiaohui Xie | Deep Implicit Functions (DIFs) represent 3D geometry with continuous signed distance functions learned through deep neural nets. Recently DIFs-based methods have been proposed to handle shape reconstruction and dense point correspondences simultaneously, capturing semantic relationships across shapes of the same class by learning a DIFs-modeled shape template. These methods provide great flexibility and accuracy in reconstructing 3D shapes and inferring correspondences. However, the point correspondences built from these methods do not intrinsically preserve the topology of the shapes, unlike mesh-based template matching methods. This limits their applications on 3D geometries where underlying topological structures exist and matter, such as anatomical structures in medical images. In this paper, we propose a new model called Neural Diffeomorphic Flow (NDF) to learn deep implicit shape templates, representing shapes as conditional diffeomorphic deformations of templates, intrinsically preserving shape topologies. The diffeomorphic deformation is realized by an auto-decoder consisting of Neural Ordinary Differential Equation (NODE) blocks that progressively map shapes to implicit templates. We conduct extensive experiments on several medical image organ segmentation datasets to evaluate the effectiveness of NDF on reconstructing and aligning shapes. NDF achieves consistently state-of-the-art organ shape reconstruction and registration results in both accuracy and quality. The source code is publicly available at https://github.com/Siwensun/Neural_Diffeomorphic_Flow--NDF. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Topology-Preserving_Shape_Reconstruction_and_Registration_via_Neural_Diffeomorphic_Flow_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Topology-Preserving_Shape_Reconstruction_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.08652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Topology-Preserving_Shape_Reconstruction_and_Registration_via_Neural_Diffeomorphic_Flow_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Topology-Preserving_Shape_Reconstruction_and_Registration_via_Neural_Diffeomorphic_Flow_CVPR_2022_paper.html | CVPR 2022 | null |
Segment and Complete: Defending Object Detectors Against Adversarial Patch Attacks With Robust Patch Detection | Jiang Liu, Alexander Levine, Chun Pong Lau, Rama Chellappa, Soheil Feizi | Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. Developing reliable defenses for object detectors against patch attacks is critical but severely understudied. In this paper, we propose Segment and Complete defense (SAC), a general framework for defending object detectors against patch attacks through detection and removal of adversarial patches. We first train a patch segmenter that outputs patch masks which provide pixel-level localization of adversarial patches. We then propose a self adversarial training algorithm to robustify the patch segmenter. In addition, we design a robust shape completion algorithm, which is guaranteed to remove the entire patch from the images if the outputs of the patch segmenter are within a certain Hamming distance of the ground-truth patch masks. Our experiments on COCO and xView datasets demonstrate that SAC achieves superior robustness even under strong adaptive attacks with no reduction in performance on clean images, and generalizes well to unseen patch shapes, attack budgets, and unseen attack methods. Furthermore, we present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. We show SAC can significantly reduce the targeted attack success rate of physical patch attacks. Our code is available at https://github.com/joellliu/SegmentAndComplete. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Segment_and_Complete_Defending_Object_Detectors_Against_Adversarial_Patch_Attacks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Segment_and_Complete_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.04532 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Segment_and_Complete_Defending_Object_Detectors_Against_Adversarial_Patch_Attacks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Segment_and_Complete_Defending_Object_Detectors_Against_Adversarial_Patch_Attacks_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Domain Few-Shot Learning With Task-Specific Adapters | Wei-Hong Li, Xialei Liu, Hakan Bilen | In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. Recent approaches broadly solve this problem by parameterizing their few-shot classifiers with task-agnostic and task-specific weights where the former is typically learned on a large training set and the latter is dynamically predicted through an auxiliary network conditioned on a small support set. In this work, we focus on the estimation of the latter, and propose to learn task-specific weights from scratch directly on a small support set, in contrast to dynamically estimating them. In particular, through systematic analysis, we show that task-specific weights through parametric adapters in matrix form with residual connections to multiple intermediate layers of a backbone network significantly improves the performance of the state-of-the-art models in the Meta-Dataset benchmark with minor additional cost. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Cross-Domain_Few-Shot_Learning_With_Task-Specific_Adapters_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Cross-Domain_Few-Shot_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2107.00358 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Domain_Few-Shot_Learning_With_Task-Specific_Adapters_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Cross-Domain_Few-Shot_Learning_With_Task-Specific_Adapters_CVPR_2022_paper.html | CVPR 2022 | null |
MAXIM: Multi-Axis MLP for Image Processing | Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li | Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for low-level vision. The inflexibility to support high-resolution images and limitations of local attention are perhaps the main bottlenecks. In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks. MAXIM uses a UNet-shaped hierarchical structure and supports long-range interactions enabled by spatially-gated MLPs. Specifically, MAXIM contains two MLP-based building blocks: a multi-axis gated MLP that allows for efficient and scalable spatial mixing of local and global visual cues, and a cross-gating block, an alternative to cross-attention, which accounts for cross-feature conditioning. Both these modules are exclusively based on MLPs, but also benefit from being both global and 'fully-convolutional', two properties that are desirable for image processing. Our extensive experimental results show that the proposed MAXIM model achieves state-of-the-art performance on more than ten benchmarks across a range of image processing tasks, including denoising, deblurring, deraining, dehazing, and enhancement while requiring fewer or comparable numbers of parameters and FLOPs than competitive models. The source code and trained models will be available at https://github.com/google-research/maxim. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tu_MAXIM_Multi-Axis_MLP_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.02973 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Part Segmentation Through Unsupervised Domain Adaptation From Synthetic Vehicles | Qing Liu, Adam Kortylewski, Zhishuai Zhang, Zizhang Li, Mengqi Guo, Qihao Liu, Xiaoding Yuan, Jiteng Mu, Weichao Qiu, Alan Yuille | Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply standard deep learning methods. In this paper, we propose the idea of learning part segmentation through unsupervised domain adaptation (UDA) from synthetic data. We first introduce UDA-Part, a comprehensive part segmentation dataset for vehicles that can serve as an adequate benchmark for UDA (https://qliu24.github.io/udapart/). In UDA-Part, we label parts on 3D CAD models which enables us to generate a large set of annotated synthetic images. We also annotate parts on a number of real images to provide a real test set. Secondly, to advance the adaptation of part models trained from the synthetic data to the real images, we introduce a new UDA algorithm that leverages the object's spatial structure to guide the adaptation process. Our experimental results on two real test datasets confirm the superiority of our approach over existing works, and demonstrate the promise of learning part segmentation for general objects from synthetic data. We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Learning_Part_Segmentation_Through_Unsupervised_Domain_Adaptation_From_Synthetic_Vehicles_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Learning_Part_Segmentation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.14098 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Part_Segmentation_Through_Unsupervised_Domain_Adaptation_From_Synthetic_Vehicles_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Learning_Part_Segmentation_Through_Unsupervised_Domain_Adaptation_From_Synthetic_Vehicles_CVPR_2022_paper.html | CVPR 2022 | null |
Delving Into the Estimation Shift of Batch Normalization in a Network | Lei Huang, Yi Zhou, Tian Wang, Jie Luo, Xianglong Liu | Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics during inference. This paper focuses on investigating the estimation of population statistics. We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones. Our primary observation is that the estimation shift can be accumulated due to the stack of BN in a network, which has detriment effects for the test performance. We further find a batch-free normalization (BFN) can block such an accumulation of estimation shift. These observations motivate our design of XBNBlock that replace one BN with BFN in the bottleneck block of residual-style networks. Experiments on the ImageNet and COCO benchmarks show that XBNBlock consistently improves the performance of different architectures, including ResNet and ResNeXt, by a significant margin and seems to be more robust to distribution shift. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Delving_Into_the_Estimation_Shift_of_Batch_Normalization_in_a_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Huang_Delving_Into_the_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.10778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Delving_Into_the_Estimation_Shift_of_Batch_Normalization_in_a_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Delving_Into_the_Estimation_Shift_of_Batch_Normalization_in_a_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Better Understanding Attribution Methods | Sukrut Rao, Moritz Böhle, Bernt Schiele | Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth attributions exist. We thus propose three novel evaluation schemes to more reliably measure the faithfulness of those methods, to make comparisons between them more fair, and to make visual inspection more systematic. To address faithfulness, we propose a novel evaluation setting (DiFull) in which we carefully control which parts of the input can influence the output in order to distinguish possible from impossible attributions. To address fairness, we note that different methods are applied at different layers, which skews any comparison, and so evaluate all methods on the same layers (ML-Att) and discuss how this impacts their performance on quantitative metrics. For more systematic visualizations, we propose a scheme (AggAtt) to qualitatively evaluate the methods on complete datasets. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods. Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rao_Towards_Better_Understanding_Attribution_Methods_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rao_Towards_Better_Understanding_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rao_Towards_Better_Understanding_Attribution_Methods_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rao_Towards_Better_Understanding_Attribution_Methods_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Object Context for Novel-View Scene Layout Generation | Xiaotian Qiao, Gerhard P. Hancke, Rynson W.H. Lau | Novel-view prediction of a scene has many applications. Existing works mainly focus on generating novel-view images via pixel-wise prediction in the image space, often resulting in severe ghosting and blurry artifacts. In this paper, we make the first attempt to explore novel-view prediction in the layout space, and introduce the new problem of novel-view scene layout generation. Given a single scene layout and the camera transformation as inputs, our goal is to generate a plausible scene layout for a specified viewpoint. Such a problem is challenging as it involves accurate understanding of the 3D geometry and semantics of the scene from as little as a single 2D scene layout. To tackle this challenging problem, we propose a deep model to capture contextualized object representation by explicitly modeling the object context transformation in the scene. The contextualized object representation is essential in generating geometrically and semantically consistent scene layouts of different views. Experiments show that our model outperforms several strong baselines on many indoor and outdoor scenes, both qualitatively and quantitatively. We also show that our model enables a wide range of applications, including novel-view image synthesis, novel-view image editing, and amodal object estimation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qiao_Learning_Object_Context_for_Novel-View_Scene_Layout_Generation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qiao_Learning_Object_Context_for_Novel-View_Scene_Layout_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qiao_Learning_Object_Context_for_Novel-View_Scene_Layout_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
PSTR: End-to-End One-Step Person Search With Transformers | Jiale Cao, Yanwei Pang, Rao Muhammad Anwer, Hisham Cholakkal, Jin Xie, Mubarak Shah, Fahad Shahbaz Khan | We propose a novel one-step transformer-based person search framework, PSTR, that jointly performs person detection and re-identification (re-id) in a single architecture. PSTR comprises a person search-specialized (PSS) module that contains a detection encoder-decoder for person detection along with a discriminative re-id decoder for person re-id. The discriminative re-id decoder utilizes a multi-level supervision scheme with a shared decoder for discriminative re-id feature learning and also comprises a part attention block to encode relationship between different parts of a person. We further introduce a simple multi-scale scheme to support re-id across person instances at different scales. PSTR jointly achieves the diverse objectives of object-level recognition (detection) and instance-level matching (re-id). To the best of our knowledge, we are the first to propose an end-to-end one-step transformer-based person search framework. Experiments are performed on two popular benchmarks: CUHK-SYSU and PRW. Our extensive ablations reveal the merits of the proposed contributions. Further, the proposed PSTR sets a new state-of-the-art on both benchmarks. On the challenging PRW benchmark, PSTR achieves a mean average precision (mAP) score of 56.5%. The source code is available at https://github.com/JialeCao001/PSTR. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cao_PSTR_End-to-End_One-Step_Person_Search_With_Transformers_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.03340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_PSTR_End-to-End_One-Step_Person_Search_With_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cao_PSTR_End-to-End_One-Step_Person_Search_With_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
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