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What Matters for Meta-Learning Vision Regression Tasks? | Ning Gao, Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann | Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. First, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. Second, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gao_What_Matters_for_Meta-Learning_Vision_Regression_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gao_What_Matters_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.04905 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_What_Matters_for_Meta-Learning_Vision_Regression_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gao_What_Matters_for_Meta-Learning_Vision_Regression_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition | Yanan Chang, Shangfei Wang | Facial action unit (AU) recognition is formulated as a supervised learning problem by recent works. However, the complex labeling process makes it challenging to provide AU annotations for large amounts of facial images. To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) to design a novel knowledge-driven self-supervised representation learning framework for AU recognition. The representation encoder is trained using large amounts of facial images without AU annotations. AU labeling rules are summarized from FACS to design facial partition manners and determine correlations between facial regions. The method utilizes a backbone network to extract local facial area representations and a project head to map the representations into a low-dimensional latent space. In the latent space, a contrastive learning component leverages the inter-area difference to learn AU-related local representations while maintaining intra-area instance discrimination. Correlations between facial regions summarized from AU labeling rules are also explored to further learn representations using a predicting learning component. Evaluation on two benchmark databases demonstrates that the learned representation is powerful and data-efficient for AU recognition. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_Knowledge-Driven_Self-Supervised_Representation_Learning_for_Facial_Action_Unit_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Selective-Supervised Contrastive Learning With Noisy Labels | Shikun Li, Xiaobo Xia, Shiming Ge, Tongliang Liu | Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated labels, which are expensive to collect. Noisy labels are more affordable, but result in corrupted representations, leading to poor generalization performance. To learn robust representations and handle noisy labels, we propose selective-supervised contrastive learning (Sel-CL) in this paper. Specifically, Sel-CL extend supervised contrastive learning (Sup-CL), which is powerful in representation learning, but is degraded when there are noisy labels. Sel-CL tackles the direct cause of the problem of Sup-CL. That is, as Sup-CL works in a pair-wise manner, noisy pairs built by noisy labels mislead representation learning. To alleviate the issue, we select confident pairs out of noisy ones for Sup-CL without knowing noise rates. In the selection process, by measuring the agreement between learned representations and given labels, we first identify confident examples that are exploited to build confident pairs. Then, the representation similarity distribution in the built confident pairs is exploited to identify more confident pairs out of noisy pairs. All obtained confident pairs are finally used for Sup-CL to enhance representations. Experiments on multiple noisy datasets demonstrate the robustness of the learned representations by our method, following the state-of-the-art performance. Source codes are available at https://github.com/ShikunLi/Sel-CL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Selective-Supervised_Contrastive_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Selective-Supervised_Contrastive_Learning_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.04181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Selective-Supervised_Contrastive_Learning_With_Noisy_Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Selective-Supervised_Contrastive_Learning_With_Noisy_Labels_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Second Order Local Anomaly for General Face Forgery Detection | Jianwei Fei, Yunshu Dai, Peipeng Yu, Tianrun Shen, Zhihua Xia, Jian Weng | In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Order Local Anomaly (SOLA) learning module to mine anomalies in local regions using deep feature maps. SOLA first decomposes the neighborhood of local features by different directions and distances and then calculates the first and second order local anomaly maps which provide more general forgery traces for the classifier. We also propose a Local Enhancement Module (LEM) to improve the discrimination between local features of real and forged regions, so as to ensure accuracy in calculating anomalies. Besides, an improved Adaptive Spatial Rich Model (ASRM) is introduced to help mine subtle noise features via learnable high pass filters. With neither pixel level annotations nor external synthetic data, our method using a simple ResNet18 backbone achieves competitive performances compared with state-of-the-art works when evaluated on unseen forgeries. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fei_Learning_Second_Order_Local_Anomaly_for_General_Face_Forgery_Detection_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fei_Learning_Second_Order_Local_Anomaly_for_General_Face_Forgery_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fei_Learning_Second_Order_Local_Anomaly_for_General_Face_Forgery_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation | Seunghun Lee, Wonhyeok Choi, Changjae Kim, Minwoo Choi, Sunghoon Im | In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models. To do so, we design a multi-target domain transfer network (MTDT-Net) that aligns visual attributes across domains by transferring the domain distinctive features through a new target adaptive denormalization (TAD) module. Moreover, we propose a bi-directional adaptive region selection (BARS) that reduces the attribute ambiguity among the class labels by adaptively selecting the regions with consistent feature statistics. We show that our single MTDT-Net can synthesize visually pleasing domain transferred images with complex driving datasets, and BARS effectively filters out the unnecessary region of training images for each target domain. With the collaboration of MTDT-Net and BARS, our ADAS achieves state-of-the-art performance for multi-target domain adaptation (MTDA). To the best of our knowledge, our method is the first MTDA method that directly adapts to multiple domains in semantic segmentation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_ADAS_A_Direct_Adaptation_Strategy_for_Multi-Target_Domain_Adaptive_Semantic_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lee_ADAS_A_Direct_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06811 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_ADAS_A_Direct_Adaptation_Strategy_for_Multi-Target_Domain_Adaptive_Semantic_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_ADAS_A_Direct_Adaptation_Strategy_for_Multi-Target_Domain_Adaptive_Semantic_CVPR_2022_paper.html | CVPR 2022 | null |
The Devil Is in the Labels: Noisy Label Correction for Robust Scene Graph Generation | Lin Li, Long Chen, Yifeng Huang, Zhimeng Zhang, Songyang Zhang, Jun Xiao | Unbiased SGG has achieved significant progress over recent years. However, almost all existing SGG models have overlooked the ground-truth annotation qualities of prevailing SGG datasets, i.e., they always assume: 1) all the manually annotated positive samples are equally correct; 2) all the un-annotated negative samples are absolutely background. In this paper, we argue that both assumptions are inapplicable to SGG: there are numerous "noisy" ground-truth predicate labels that break these two assumptions, and these noisy samples actually harm the training of unbiased SGG models. To this end, we propose a novel model-agnostic NoIsy label CorrEction strategy for SGG: NICE. NICE can not only detect noisy samples but also reassign more high-quality predicate labels to them. After the NICE training, we can obtain a cleaner version of SGG dataset for model training. Specifically, NICE consists of three components: negative Noisy Sample Detection (Neg-NSD), positive NSD (Pos-NSD), and Noisy Sample Correction (NSC). Firstly, in Neg-NSD, we formulate this task as an out-of-distribution detection problem, and assign pseudo labels to all detected noisy negative samples. Then, in Pos-NSD, we use a clustering-based algorithm to divide all positive samples into multiple sets, and treat the samples in the noisiest set as noisy positive samples. Lastly, in NSC, we use a simple but effective weighted KNN to reassign new predicate labels to noisy positive samples. Extensive results on different backbones and tasks have attested to the effectiveness and generalization abilities of each component of NICE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_The_Devil_Is_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_The_Devil_Is_in_the_Labels_Noisy_Label_Correction_for_CVPR_2022_paper.html | CVPR 2022 | null |
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation | Zhao Yang, Jiaqi Wang, Yansong Tang, Kai Chen, Hengshuang Zhao, Philip H.S. Torr | Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key challenges behind this task is leveraging the referring expression for highlighting relevant positions in the image. A paradigm for tackling this problem is to leverage a powerful vision-language ("cross-modal") decoder to fuse features independently extracted from a vision encoder and a language encoder. Recent methods have made remarkable advancements in this paradigm by exploiting Transformers as cross-modal decoders, concurrent to the Transformer's overwhelming success in many other vision-language tasks. Adopting a different approach in this work, we show that significantly better cross-modal alignments can be achieved through the early fusion of linguistic and visual features in intermediate layers of a vision Transformer encoder network. By conducting cross-modal feature fusion in the visual feature encoding stage, we can leverage the well-proven correlation modeling power of a Transformer encoder for excavating helpful multi-modal context. This way, accurate segmentation results are readily harvested with a light-weight mask predictor. Without bells and whistles, our method surpasses the previous state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_LAVT_Language-Aware_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
SimT: Handling Open-Set Noise for Domain Adaptive Semantic Segmentation | Xiaoqing Guo, Jie Liu, Tongliang Liu, Yixuan Yuan | This paper studies a practical domain adaptative (DA) semantic segmentation problem where only pseudo-labeled target data is accessible through a black-box model. Due to the domain gap and label shift between two domains, pseudo-labeled target data contains mixed closed-set and open-set label noises. In this paper, we propose a simplex noise transition matrix (SimT) to model the mixed noise distributions in DA semantic segmentation and formulate the problem as estimation of SimT. By exploiting computational geometry analysis and properties of segmentation, we design three complementary regularizers, i.e. volume regularization, anchor guidance, convex guarantee, to approximate the true SimT. Specifically, volume regularization minimizes the volume of simplex formed by rows of the non-square SimT, which ensures outputs of segmentation model to fit into the ground truth label distribution. To compensate for the lack of open-set knowledge, anchor guidance and convex guarantee are devised to facilitate the modeling of open-set noise distribution and enhance the discriminative feature learning among closed-set and open-set classes. The estimated SimT is further utilized to correct noise issues in pseudo labels and promote the generalization ability of segmentation model on target domain data. Extensive experimental results demonstrate that the proposed SimT can be flexibly plugged into existing DA methods to boost the performance. The source code is available at https://github.com/CityU-AIM-Group/SimT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guo_SimT_Handling_Open-Set_Noise_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Guo_SimT_Handling_Open-Set_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_SimT_Handling_Open-Set_Noise_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guo_SimT_Handling_Open-Set_Noise_for_Domain_Adaptive_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Interspace Pruning: Using Adaptive Filter Representations To Improve Training of Sparse CNNs | Paul Wimmer, Jens Mehnert, Alexandru Condurache | Unstructured pruning is well suited to reduce the memory footprint of convolutional neural networks (CNNs), both at training and inference time. CNNs contain parameters arranged in K x K filters. Standard unstructured pruning (SP) reduces the memory footprint of CNNs by setting filter elements to zero, thereby specifying a fixed subspace that constrains the filter. Especially if pruning is applied before or during training, this induces a strong bias. To overcome this, we introduce interspace pruning (IP), a general tool to improve existing pruning methods. It uses filters represented in a dynamic interspace by linear combinations of an underlying adaptive filter basis (FB). For IP, FB coefficients are set to zero while un-pruned coefficients and FBs are trained jointly. In this work, we provide mathematical evidence for IP's superior performance and demonstrate that IP outperforms SP on all tested state-of-the-art unstructured pruning methods. Especially in challenging situations, like pruning for ImageNet or pruning to high sparsity, IP greatly exceeds SP with equal runtime and parameter costs. Finally, we show that advances of IP are due to improved trainability and superior generalization ability. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wimmer_Interspace_Pruning_Using_Adaptive_Filter_Representations_To_Improve_Training_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wimmer_Interspace_Pruning_Using_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wimmer_Interspace_Pruning_Using_Adaptive_Filter_Representations_To_Improve_Training_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wimmer_Interspace_Pruning_Using_Adaptive_Filter_Representations_To_Improve_Training_of_CVPR_2022_paper.html | CVPR 2022 | null |
PLAD: Learning To Infer Shape Programs With Pseudo-Labels and Approximate Distributions | R. Kenny Jones, Homer Walke, Daniel Ritchie | Inferring programs which generate 2D and 3D shapes is important for reverse engineering, editing, and more. Training models to perform this task is complicated because paired (shape, program) data is not readily available for many domains, making exact supervised learning infeasible. However, it is possible to get paired data by compromising the accuracy of either the assigned program labels or the shape distribution. Wake-sleep methods use samples from a generative model of shape programs to approximate the distribution of real shapes. In self-training, shapes are passed through a recognition model, which predicts programs that are treated as 'pseudo-labels' for those shapes. Related to these approaches, we introduce a novel self-training variant unique to program inference, where program pseudo-labels are paired with their executed output shapes, avoiding label mismatch at the cost of an approximate shape distribution. We propose to group these regimes under a single conceptual framework, where training is performed with maximum likelihood updates sourced from either Pseudo-Labels or an Approximate Distribution (PLAD). We evaluate these techniques on multiple 2D and 3D shape program inference domains. Compared with policy gradient reinforcement learning, we show that PLAD techniques infer more accurate shape programs and converge significantly faster. Finally, we propose to combine updates from different PLAD methods within the training of a single model, and find that this approach outperforms any individual technique. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jones_PLAD_Learning_To_Infer_Shape_Programs_With_Pseudo-Labels_and_Approximate_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jones_PLAD_Learning_To_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2011.13045 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jones_PLAD_Learning_To_Infer_Shape_Programs_With_Pseudo-Labels_and_Approximate_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jones_PLAD_Learning_To_Infer_Shape_Programs_With_Pseudo-Labels_and_Approximate_CVPR_2022_paper.html | CVPR 2022 | null |
PTTR: Relational 3D Point Cloud Object Tracking With Transformer | Changqing Zhou, Zhipeng Luo, Yueru Luo, Tianrui Liu, Liang Pan, Zhongang Cai, Haiyu Zhao, Shijian Lu | In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in the current search point cloud given a template point cloud. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve relevant points to given templates during subsampling. 2) Furthermore, we propose a Point Relation Transformer (PRT) consisting of a self-attention and a cross-attention module. The global self-attention operation captures long-range dependencies to enhance encoded point features for the search area and the template, respectively. Subsequently, we generate the coarse tracking results by matching the two sets of point features via cross-attention. 3) Based on the coarse tracking results, we employ a novel Prediction Refinement Module to obtain the final refined prediction. In addition, we create a large-scale point cloud single object tracking benchmark based on the Waymo Open Dataset. Extensive experiments show that PTTR achieves superior point cloud tracking in both accuracy and efficiency. Our code and dataset will be released upon acceptance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_PTTR_Relational_3D_Point_Cloud_Object_Tracking_With_Transformer_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.02857 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_PTTR_Relational_3D_Point_Cloud_Object_Tracking_With_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_PTTR_Relational_3D_Point_Cloud_Object_Tracking_With_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Frequency-Driven Imperceptible Adversarial Attack on Semantic Similarity | Cheng Luo, Qinliang Lin, Weicheng Xie, Bizhu Wu, Jinheng Xie, Linlin Shen | Current adversarial attack research reveals the vulnerability of learning-based classifiers against carefully crafted perturbations. However, most existing attack methods have inherent limitations in cross-dataset generalization as they rely on a classification layer with a closed set of categories. Furthermore, the perturbations generated by these methods may appear in regions easily perceptible to the human visual system (HVS). To circumvent the former problem, we propose a novel algorithm that attacks semantic similarity on feature representations. In this way, we are able to fool classifiers without limiting attacks to a specific dataset. For imperceptibility, we introduce the low-frequency constraint to limit perturbations within high-frequency components, ensuring perceptual similarity between adversarial examples and originals. Extensive experiments on three datasets (CIFAR-10, CIFAR-100, and ImageNet-1K) and three public online platforms indicate that our attack can yield misleading and transferable adversarial examples across architectures and datasets. Additionally, visualization results and quantitative performance (in terms of four different metrics) show that the proposed algorithm generates more imperceptible perturbations than the state-of-the-art methods. Code is made available at https://github.com/LinQinLiang/SSAH-adversarial-attack. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Frequency-Driven_Imperceptible_Adversarial_Attack_on_Semantic_Similarity_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_Frequency-Driven_Imperceptible_Adversarial_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.05151 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Frequency-Driven_Imperceptible_Adversarial_Attack_on_Semantic_Similarity_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Frequency-Driven_Imperceptible_Adversarial_Attack_on_Semantic_Similarity_CVPR_2022_paper.html | CVPR 2022 | null |
ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds | Georg Bökman, Fredrik Kahl, Axel Flinth | In this paper, we are concerned with rotation equivariance on 2D point cloud data. We describe a particular set of functions able to approximate any continuous rotation equivariant and permutation invariant function. Based on this result, we propose a novel neural network architecture for processing 2D point clouds and we prove its universality for approximating functions exhibiting these symmetries. We also show how to extend the architecture to accept a set of 2D-2D correspondences as indata, while maintaining similar equivariance properties. Experiments are presented on the estimation of essential matrices in stereo vision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bokman_ZZ-Net_A_Universal_Rotation_Equivariant_Architecture_for_2D_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bokman_ZZ-Net_A_Universal_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bokman_ZZ-Net_A_Universal_Rotation_Equivariant_Architecture_for_2D_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bokman_ZZ-Net_A_Universal_Rotation_Equivariant_Architecture_for_2D_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
Video Demoireing With Relation-Based Temporal Consistency | Peng Dai, Xin Yu, Lan Ma, Baoheng Zhang, Jia Li, Wenbo Li, Jiajun Shen, Xiaojuan Qi | Moire patterns, appearing as color distortions, severely degrade the image and video qualities when filming a screen with digital cameras. Considering the increasing demands for capturing videos, we study how to remove such undesirable moire patterns in videos, namely video demoireing. To this end, we introduce the first hand-held video demoireing dataset with a dedicated data collection pipeline to ensure spatial and temporal alignments of captured data. Further, a baseline video demoireing model with implicit feature space alignment and selective feature aggregation is developed to leverage complementary information from nearby frames to improve frame-level video demoireing. More importantly, we propose a relation-based temporal consistency loss to encourage the model to learn temporal consistency priors directly from ground-truth reference videos, which facilitates producing temporally consistent predictions and effectively maintains frame-level qualities. Extensive experiments manifest the superiority of our model. Code is available at https://daipengwa.github.io/VDmoire_ProjectPage/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_Video_Demoireing_With_Relation-Based_Temporal_Consistency_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.02957 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_Video_Demoireing_With_Relation-Based_Temporal_Consistency_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_Video_Demoireing_With_Relation-Based_Temporal_Consistency_CVPR_2022_paper.html | CVPR 2022 | null |
Co-Domain Symmetry for Complex-Valued Deep Learning | Utkarsh Singhal, Yifei Xing, Stella X. Yu | We study complex-valued scaling as a type of symmetry natural and unique to complex-valued measurements and representations. Deep Complex Networks (DCN) extends real-valued algebra to the complex domain without addressing complex-valued scaling. SurReal extends manifold learning to the complex plane, achieving scaling invariance using distances that discard phase information. Treating complex-valued scaling as a co-domain transformation, we design novel equivariant/invariant neural network layer functions and construct architectures that exploit co-domain symmetry. We also propose novel complex-valued representations of RGB images, where complex-valued scaling indicates hue shift or correlated changes across color channels. Benchmarked on MSTAR, CIFAR10, CIFAR100, and SVHN, our co-domain symmetric (CDS) classifiers deliver higher accuracy, better generalization, more robustness to co-domain transformations, and lower model bias and variance than DCN and SurReal with far fewer parameters. | https://openaccess.thecvf.com/content/CVPR2022/papers/Singhal_Co-Domain_Symmetry_for_Complex-Valued_Deep_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.01525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Singhal_Co-Domain_Symmetry_for_Complex-Valued_Deep_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Singhal_Co-Domain_Symmetry_for_Complex-Valued_Deep_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Industrial Style Transfer With Large-Scale Geometric Warping and Content Preservation | Jinchao Yang, Fei Guo, Shuo Chen, Jun Li, Jian Yang | We propose a novel style transfer method to quickly create a new visual product with a nice appearance for industrial designers' reference. Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product. Our model, Industrial Style Transfer (InST), consists of large-scale geometric warping (LGW) and interest-consistency texture transfer (ICTT). LGW aims to explore an unsupervised transformation between the shape masks of the source and target products for fitting large-scale shape warping. Furthermore, we introduce a mask smoothness regularization term to prevent the abrupt changes of the details of the source product. ICTT introduces an interest regularization term to maintain important contents of the warped product when it is stylized by using the art style image. Extensive experimental results demonstrate that InST achieves state-of-the-art performance on multiple visual product design tasks, e.g., companies' snail logos and classical bottles (please see Fig. 1). To the best of our knowledge, we are the first to extend the neural style transfer method to create industrial product appearances. Code is available at https://jcyang98.github.io/InST/home.html | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Industrial_Style_Transfer_With_Large-Scale_Geometric_Warping_and_Content_Preservation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Industrial_Style_Transfer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Industrial_Style_Transfer_With_Large-Scale_Geometric_Warping_and_Content_Preservation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Industrial_Style_Transfer_With_Large-Scale_Geometric_Warping_and_Content_Preservation_CVPR_2022_paper.html | CVPR 2022 | null |
Modeling Image Composition for Complex Scene Generation | Zuopeng Yang, Daqing Liu, Chaoyue Wang, Jie Yang, Dacheng Tao | We present a method that achieves state-of-the-art results on challenging (few-shot) layout-to-image generation tasks by accurately modeling textures, structures and relationships contained in a complex scene. After compressing RGB images into patch tokens, we propose the Transformer with Focal Attention (TwFA) for exploring dependencies of object-to-object, object-to-patch and patch-to-patch. Compared to existing CNN-based and Transformer-based generation models that entangled modeling on pixel-level & patch-level and object-level & patch-level respectively, the proposed focal attention predicts the current patch token by only focusing on its highly-related tokens that specified by the spatial layout, thereby achieving disambiguation during training. Furthermore, the proposed TwFA largely increases the data efficiency during training, therefore we propose the first few-shot complex scene generation strategy based on the well-trained TwFA. Comprehensive experiments show the superiority of our method, which significantly increases both quantitative metrics and qualitative visual realism with respect to state-of-the-art CNN-based and transformer-based methods. Code is available at https://github.com/JohnDreamer/TwFA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Modeling_Image_Composition_for_Complex_Scene_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_Modeling_Image_Composition_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2206.00923 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Modeling_Image_Composition_for_Complex_Scene_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Modeling_Image_Composition_for_Complex_Scene_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
SS3D: Sparsely-Supervised 3D Object Detection From Point Cloud | Chuandong Liu, Chenqiang Gao, Fangcen Liu, Jiang Liu, Deyu Meng, Xinbo Gao | Conventional deep learning based methods for 3D object detection require a large amount of 3D bounding box annotations for training, which is expensive to obtain in practice. Sparsely annotated object detection, which can largely reduce the annotations, is very challenging since the missingannotated instances would be regarded as the background during training. In this paper, we propose a sparselysupervised 3D object detection method, named SS3D. Aiming to eliminate the negative supervision caused by the missing annotations, we design a missing-annotated instance mining module with strict filtering strategies to mine positive instances. In the meantime, we design a reliable background mining module and a point cloud filling data augmentation strategy to generate the confident data for iteratively learning with reliable supervision. The proposed SS3D is a general framework that can be used to learn any modern 3D object detector. Extensive experiments on the KITTI dataset reveal that on different 3D detectors, the proposed SS3D framework with only 20% annotations required can achieve on-par performance comparing to fullysupervised methods. Comparing with the state-of-the-art semi-supervised 3D objection detection on KITTI, our SS3D improves the benchmarks by significant margins under the same annotation workload. Moreover, our SS3D also outperforms the state-of-the-art weakly-supervised method by remarkable margins, highlighting its effectiveness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_SS3D_Sparsely-Supervised_3D_Object_Detection_From_Point_Cloud_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_SS3D_Sparsely-Supervised_3D_Object_Detection_From_Point_Cloud_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_SS3D_Sparsely-Supervised_3D_Object_Detection_From_Point_Cloud_CVPR_2022_paper.html | CVPR 2022 | null |
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer | Wenjian Wang, Lijuan Duan, Yuxi Wang, Qing En, Junsong Fan, Zhaoxiang Zhang | Few-shot semantic segmentation intends to predict pixel level categories using only a few labeled samples. Existing few-shot methods focus primarily on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured. The actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we propose an interesting and challenging cross-domain few-shot semantic segmentation task, where the training and test tasks perform on different domains. Specifically, we first propose a meta-memory bank to improve the generalization of the segmentation network by bridging the domain gap between source and target domains. The meta-memory stores the intra-domain style information from source domain instances and transfers it to target samples. Subsequently, we adopt a new contrastive learning strategy to explore the knowledge of different categories during the training stage. The negative and positive pairs are obtained from the proposed memory-based style augmentation. Comprehensive experiments demonstrate that our proposed method achieves promising results on cross-domain few-shot semantic segmentation tasks on COCO-20, PASCAL-5, FSS-1000, and SUIM datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Remember_the_Difference_Cross-Domain_Few-Shot_Semantic_Segmentation_via_Meta-Memory_Transfer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Remember_the_Difference_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Remember_the_Difference_Cross-Domain_Few-Shot_Semantic_Segmentation_via_Meta-Memory_Transfer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Remember_the_Difference_Cross-Domain_Few-Shot_Semantic_Segmentation_via_Meta-Memory_Transfer_CVPR_2022_paper.html | CVPR 2022 | null |
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation | Yu Deng, Jiaolong Yang, Jianfeng Xiang, Xin Tong | 3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still cannot generate highly-realistic images with fine details. A critical reason is that the high memory and computation cost of volumetric representation learning greatly restricts the number of point samples for radiance integration during training. Deficient sampling not only limits the expressive power of the generator to handle fine details but also impedes effective GAN training due to the noise caused by unstable Monte Carlo sampling. We propose a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume. For each viewing ray, we calculate ray-surface intersections and accumulate their radiance generated by the network. By training and rendering such radiance manifolds, our generator can produce high quality images with realistic fine details and strong visual 3D consistency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_GRAM_Generative_Radiance_Manifolds_for_3D-Aware_Image_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Deng_GRAM_Generative_Radiance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.08867 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_GRAM_Generative_Radiance_Manifolds_for_3D-Aware_Image_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_GRAM_Generative_Radiance_Manifolds_for_3D-Aware_Image_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
UniVIP: A Unified Framework for Self-Supervised Visual Pre-Training | Zhaowen Li, Yousong Zhu, Fan Yang, Wei Li, Chaoyang Zhao, Yingying Chen, Zhiyang Chen, Jiahao Xie, Liwei Wu, Rui Zhao, Ming Tang, Jinqiao Wang | Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instance-instance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that UniVIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_UniVIP_A_Unified_Framework_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.06965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_UniVIP_A_Unified_Framework_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_UniVIP_A_Unified_Framework_for_Self-Supervised_Visual_Pre-Training_CVPR_2022_paper.html | CVPR 2022 | null |
GraFormer: Graph-Oriented Transformer for 3D Pose Estimation | Weixi Zhao, Weiqiang Wang, Yunjie Tian | In 2D-to-3D pose estimation, it is important to exploit the spatial constraints of 2D joints, but it is not yet well modeled. To better model the relation of joints for 3D pose estimation, we propose an effective but simple network, called GraFormer, where a novel transformer architecture is designed via embedding graph convolution layers after multi-head attention block. The proposed GraFormer is built by repeatedly stacking the GraAttention block and the ChebGConv block. The proposed GraAttention block is a new transformer block designed for processing graph-structured data, which is able to learn better features through capturing global information from all the nodes as well as the explicit adjacency structure of nodes. To model the implicit high-order connection relations among non-neighboring nodes, the ChebGConv block is introduced to exchange information between non-neighboring nodes and attain a larger receptive field. We have empirically shown the superiority of GraFormer through extensive experiments on popular public datasets. Specifically, GraFormer outperforms the state-of-the-art GraghSH on the Human3.6M dataset yet only contains 18% parameters of it | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_GraFormer_Graph-Oriented_Transformer_for_3D_Pose_Estimation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_GraFormer_Graph-Oriented_Transformer_for_3D_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_GraFormer_Graph-Oriented_Transformer_for_3D_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Decoupling Zero-Shot Semantic Segmentation | Jian Ding, Nan Xue, Gui-Song Xia, Dengxin Dai | Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not been seen in the training. Existing works formulate ZS3 as a pixel-level zero-shot classification problem, and transfer semantic knowledge from seen classes to unseen ones with the help of language models pre-trained only with texts. While simple, the pixel-level ZS3 formulation shows the limited capability to integrate vision-language models that are often pre-trained with image-text pairs and currently demonstrate great potential for vision tasks. Inspired by the observation that humans often perform segment-level semantic labeling, we propose to decouple the ZS3 into two sub-tasks: 1) a class-agnostic grouping task to group the pixels into segments. 2) a zero-shot classification task on segments. The former task does not involve category information and can be directly transferred to group pixels for unseen classes. The latter task performs at segment-level and provides a natural way to leverage large-scale vision-language models pre-trained with image-text pairs (e.g. CLIP) for ZS3. Based on the decoupling formulation, we propose a simple and effective zero-shot semantic segmentation model, called ZegFormer, which outperforms the previous methods on ZS3 standard benchmarks by large margins, e.g., 22 points on the PAS-CAL VOC and 3 points on the COCO-Stuff in terms of mIoU for unseen classes. Code will be released at https://github.com/dingjiansw101/ZegFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Decoupling_Zero-Shot_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Decoupling_Zero-Shot_Semantic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.07910 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Decoupling_Zero-Shot_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Decoupling_Zero-Shot_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Neural Collaborative Graph Machines for Table Structure Recognition | Hao Liu, Xin Li, Bing Liu, Deqiang Jiang, Yinsong Liu, Bo Ren | Recently, table structure recognition has achieved impressive progress with the help of deep graph models. Most of them exploit single visual cues of tabular elements or simply combine visual cues with other modalities via early fusion to reason their graph relationships. However, neither early fusion nor individually reasoning in terms of multiple modalities can be appropriate for all varieties of table structures with great diversity. Instead, different modalities are expected to collaborate with each other in different patterns for different table cases. In the community, the importance of intra-inter modality interactions for table structure reasoning is still unexplored. In this paper, we define it as heterogeneous table structure recognition (Hetero-TSR) problem. With the aim of filling this gap, we present a novel Neural Collaborative Graph Machines (NCGM) equipped with stacked collaborative blocks, which alternatively extracts intra-modality context and models inter-modality interactions in a hierarchical way. It can represent the intra-inter modality relationships of tabular elements more robustly, which significantly improves the recognition performance. We also show that the proposed NCGM can modulate collaborative pattern of different modalities conditioned on the context of intra-modality cues, which is vital for diversified table cases. Experimental results on benchmarks demonstrate our proposed NCGM achieves state-of-the-art performance and beats other contemporary methods by a large margin especially under challenging scenarios. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Neural_Collaborative_Graph_Machines_for_Table_Structure_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Neural_Collaborative_Graph_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Collaborative_Graph_Machines_for_Table_Structure_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Neural_Collaborative_Graph_Machines_for_Table_Structure_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Robust Vision Transformer | Xiaofeng Mao, Gege Qi, Yuefeng Chen, Xiaodan Li, Ranjie Duan, Shaokai Ye, Yuan He, Hui Xue | Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and computation cost, lacking the investigation of the intrinsic influence on model robustness and generalization. In this work, we conduct systematic evaluation on components of ViTs in terms of their impact on robustness to adversarial examples, common corruptions and distribution shifts. We find some components can be harmful to robustness. By leveraging robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness. Inspired by the findings during the evaluation, we further propose two new plug-and-play techniques called position-aware attention scaling and patch-wise augmentation to augment our RVT, which we abbreviate as RVT*. The experimental results of RVT on ImageNet and six robustness benchmarks demonstrate its advanced robustness and generalization ability compared with previous ViTs and state-of-the-art CNNs. Furthermore, RVT-S* achieves Top-1 rank on multiple robustness leaderboards including ImageNet-C, ImageNet-Sketch and ImageNet-R. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mao_Towards_Robust_Vision_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mao_Towards_Robust_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2105.07926 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mao_Towards_Robust_Vision_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mao_Towards_Robust_Vision_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
DeepCurrents: Learning Implicit Representations of Shapes With Boundaries | David Palmer, Dmitriy Smirnov, Stephanie Wang, Albert Chern, Justin Solomon | Recent techniques have been successful in reconstructing surfaces as level sets of learned functions (such as signed distance fields) parameterized by deep neural networks. Many of these methods, however, learn only closed surfaces and are unable to reconstruct shapes with boundary curves. We propose a hybrid shape representation that combines explicit boundary curves with implicit learned interiors. Using machinery from geometric measure theory, we parameterize currents using deep networks and use stochastic gradient descent to solve a minimal surface problem. By modifying the metric according to target geometry coming, e.g., from a mesh or point cloud, we can use this approach to represent arbitrary surfaces, learning implicitly defined shapes with explicitly defined boundary curves. We further demonstrate learning families of shapes jointly parameterized by boundary curves and latent codes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Palmer_DeepCurrents_Learning_Implicit_Representations_of_Shapes_With_Boundaries_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.09383 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Palmer_DeepCurrents_Learning_Implicit_Representations_of_Shapes_With_Boundaries_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Palmer_DeepCurrents_Learning_Implicit_Representations_of_Shapes_With_Boundaries_CVPR_2022_paper.html | CVPR 2022 | null |
Learning Affordance Grounding From Exocentric Images | Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, Dacheng Tao | Affordance grounding, a task to ground (i.e., localize) action possibility region in objects, which faces the challenge of establishing an explicit link with object parts due to the diversity of interactive affordance. Human has the ability that transform the various exocentric interactions to invariant egocentric affordance so as to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. To this end, we devise a cross-view knowledge transfer framework that extracts affordance-specific features from exocentric interactions and enhances the perception of affordance regions by preserving affordance correlation. Specifically, an Affordance Invariance Mining module is devised to extract specific clues by minimizing the intra-class differences originated from interaction habits in exocentric images. Besides, an Affordance Co-relation Preserving strategy is presented to perceive and localize affordance by aligning the co-relation matrix of predicted results between the two views. Particularly, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from 36 affordance categories. Experimental results demonstrate that our method outperforms the representative models in terms of objective metrics and visual quality. Code: github.com/lhc1224/Cross-View-AG. | https://openaccess.thecvf.com/content/CVPR2022/papers/Luo_Learning_Affordance_Grounding_From_Exocentric_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Luo_Learning_Affordance_Grounding_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09905 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Learning_Affordance_Grounding_From_Exocentric_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Luo_Learning_Affordance_Grounding_From_Exocentric_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions | Van Nguyen Nguyen, Yinlin Hu, Yang Xiao, Mathieu Salzmann, Vincent Lepetit | We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects. In contrast with the state-of-the-art methods, the new objects on which our method is applied can be very different from the training objects. As a result, we are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous template-based approaches further confirms the benefits of local features for template matching. We outperform the state-of-the-art template matching methods on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data are publicly available at https://github.com/nv-nguyen/template-pose | https://openaccess.thecvf.com/content/CVPR2022/papers/Nguyen_Templates_for_3D_Object_Pose_Estimation_Revisited_Generalization_to_New_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Nguyen_Templates_for_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17234 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Nguyen_Templates_for_3D_Object_Pose_Estimation_Revisited_Generalization_to_New_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Nguyen_Templates_for_3D_Object_Pose_Estimation_Revisited_Generalization_to_New_CVPR_2022_paper.html | CVPR 2022 | null |
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability | Yifeng Xiong, Jiadong Lin, Min Zhang, John E. Hopcroft, Kun He | The black-box adversarial attack has attracted impressive attention for its practical use in the field of deep learning security. Meanwhile, it is very challenging as there is no access to the network architecture or internal weights of the target model. Based on the hypothesis that if an example remains adversarial for multiple models, then it is more likely to transfer the attack capability to other models, the ensemble-based adversarial attack methods are efficient and widely used for black-box attacks. However, ways of ensemble attack are rather less investigated, and existing ensemble attacks simply fuse the outputs of all the models evenly. In this work, we treat the iterative ensemble attack as a stochastic gradient descent optimization process, in which the variance of the gradients on different models may lead to poor local optima. To this end, we propose a novel attack method called the stochastic variance reduced ensemble (SVRE) attack, which could reduce the gradient variance of the ensemble models and take full advantage of the ensemble attack. Empirical results on the standard ImageNet dataset demonstrate that the proposed method could boost the adversarial transferability and outperforms existing ensemble attacks significantly. Code is available at https://github.com/JHL-HUST/SVRE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiong_Stochastic_Variance_Reduced_Ensemble_Adversarial_Attack_for_Boosting_the_Adversarial_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiong_Stochastic_Variance_Reduced_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.10752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiong_Stochastic_Variance_Reduced_Ensemble_Adversarial_Attack_for_Boosting_the_Adversarial_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiong_Stochastic_Variance_Reduced_Ensemble_Adversarial_Attack_for_Boosting_the_Adversarial_CVPR_2022_paper.html | CVPR 2022 | null |
Unknown-Aware Object Detection: Learning What You Don't Know From Videos in the Wild | Xuefeng Du, Xin Wang, Gabriel Gozum, Yixuan Li | Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical yet underexplored. One of the key challenges is that models lack supervision signals from unknown data, producing overconfident predictions on OOD objects. We propose a new unknown-aware object detection framework through Spatial-Temporal Unknown Distillation (STUD), which distills unknown objects from videos in the wild and meaningfully regularizes the model's decision boundary. STUD first identifies the unknown candidate object proposals in the spatial dimension, and then aggregates the candidates across multiple video frames to form a diverse set of unknown objects near the decision boundary. Alongside, we employ an energy-based uncertainty regularization loss, which contrastively shapes the uncertainty space between the in-distribution and distilled unknown objects. STUD establishes the state-of-the-art performance on OOD detection tasks for object detection, reducing the FPR95 score by over 10% compared to the previous best method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Du_Unknown-Aware_Object_Detection_Learning_What_You_Dont_Know_From_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Du_Unknown-Aware_Object_Detection_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Unknown-Aware_Object_Detection_Learning_What_You_Dont_Know_From_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Du_Unknown-Aware_Object_Detection_Learning_What_You_Dont_Know_From_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Modal Extreme Classification | Anshul Mittal, Kunal Dahiya, Shreya Malani, Janani Ramaswamy, Seba Kuruvilla, Jitendra Ajmera, Keng-hao Chang, Sumeet Agarwal, Purushottam Kar, Manik Varma | This paper develops the MUFIN technique for extreme classification (XC) tasks with millions of labels where datapoints and labels are endowed with visual and textual descriptors. Applications of MUFIN to product-to-product recommendation and bid query prediction over several millions of products are presented. Contemporary multi-modal methods frequently rely on purely embedding-based methods. On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding-only methods but mostly focus on text-based categorization tasks. MUFIN bridges this gap by reformulating multi-modal categorization as an XC problem with several millions of labels. This presents the twin challenges of developing multi-modal architectures that can offer embeddings sufficiently expressive to allow accurate categorization over millions of labels; and training and inference routines that scale logarithmically in the number of labels. MUFIN develops an architecture based on cross-modal attention and trains it in a modular fashion using pre-training and positive and negative mining. A novel product-to-product recommendation dataset MM-AmazonTitles-300K containing over 300K products was curated from publicly available amazon.com listings with each product endowed with a title and multiple images. On the MM-AmazonTitles-300K and Polyvore datasets, and a dataset with over 4 million labels curated from click logs of the Bing search engine, MUFIN offered at least 3% higher accuracy than leading text-based, image-based and multi-modal techniques. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mittal_Multi-Modal_Extreme_Classification_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mittal_Multi-Modal_Extreme_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mittal_Multi-Modal_Extreme_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement | Ankit Goyal, Arsalan Mousavian, Chris Paxton, Yu-Wei Chao, Brian Okorn, Jia Deng, Dieter Fox | Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, an end-to-end method for the challenging problem of object rearrangement for unknown objects given an RGBD image of the original and final scenes. First, we learn an optical flow model based on RAFT to estimate the relative transformation of the objects purely from synthetic data. This flow is then used in an iterative minimization algorithm to achieve accurate positioning of previously unseen objects. Crucially, we show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data. Videos are available at https://imankgoyal.github.io/ifor.html. | https://openaccess.thecvf.com/content/CVPR2022/papers/Goyal_IFOR_Iterative_Flow_Minimization_for_Robotic_Object_Rearrangement_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Goyal_IFOR_Iterative_Flow_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2202.00732 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Goyal_IFOR_Iterative_Flow_Minimization_for_Robotic_Object_Rearrangement_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Goyal_IFOR_Iterative_Flow_Minimization_for_Robotic_Object_Rearrangement_CVPR_2022_paper.html | CVPR 2022 | null |
Training-Free Transformer Architecture Search | Qinqin Zhou, Kekai Sheng, Xiawu Zheng, Ke Li, Xing Sun, Yonghong Tian, Jie Chen, Rongrong Ji | Recently, Vision Transformer (ViT) has achieved remarkable success in several computer vision tasks. The progresses are highly relevant to the architecture design, then it is worthwhile to propose Transformer Architecture Search (TAS) to search for better ViTs automatically. However, current TAS methods are time-consuming and existing zero-cost proxies in CNN do not generalize well to the ViT search space according to our experimental observations. In this paper, for the first time, we investigate how to conduct TAS in a training-free manner and devise an effective training-free TAS (TF-TAS) scheme. Firstly, we observe that the properties of multi-head self-attention (MSA) and multi-layer perceptron (MLP) in ViTs are quite different and that the synaptic diversity of MSA affects the performance notably. Secondly, based on the observation, we devise a modular strategy in TF-TAS that evaluates and ranks ViT architectures from two theoretical perspectives: synaptic diversity and synaptic saliency, termed as DSS-indicator. With DSS-indicator, evaluation results are strongly correlated with the test accuracies of ViT models. Experimental results demonstrate that our TF-TAS achieves a competitive performance against the state-of-the-art manually or automatically design ViT architectures, and it promotes the searching efficiency in ViT search space greatly: from about 24 GPU days to less than 0.5 GPU days. Moreover, the proposed DSS-indicator outperforms the existing cutting-edge zero-cost approaches (e.g., TE-score and NASWOT). | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Training-Free_Transformer_Architecture_Search_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Training-Free_Transformer_Architecture_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Training-Free_Transformer_Architecture_Search_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Training-Free_Transformer_Architecture_Search_CVPR_2022_paper.html | CVPR 2022 | null |
Zero Experience Required: Plug & Play Modular Transfer Learning for Semantic Visual Navigation | Ziad Al-Halah, Santhosh Kumar Ramakrishnan, Kristen Grauman | In reinforcement learning for visual navigation, it is common to develop a model for each new task, and train that model from scratch with task-specific interactions in 3D environments. However, this process is expensive; massive amounts of interactions are needed for the model to generalize well. Moreover, this process is repeated whenever there is a change in the task type or the goal modality. We present a unified approach to visual navigation using a novel modular transfer learning model. Our model can effectively leverage its experience from one source task and apply it to multiple target tasks (e.g., ObjectNav, RoomNav, ViewNav) with various goal modalities (e.g., image, sketch, audio, label). Furthermore, our model enables zero-shot experience learning, whereby it can solve the target tasks without receiving any task-specific interactive training. Our experiments on multiple photorealistic datasets and challenging tasks show that our approach learns faster, generalizes better, and outperforms SoTA models by a significant margin. Project page: https://vision.cs.utexas.edu/projects/zsel/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Al-Halah_Zero_Experience_Required_Plug__Play_Modular_Transfer_Learning_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Al-Halah_Zero_Experience_Required_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Al-Halah_Zero_Experience_Required_Plug__Play_Modular_Transfer_Learning_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Al-Halah_Zero_Experience_Required_Plug__Play_Modular_Transfer_Learning_for_CVPR_2022_paper.html | CVPR 2022 | null |
Non-Isotropy Regularization for Proxy-Based Deep Metric Learning | Karsten Roth, Oriol Vinyals, Zeynep Akata | Deep Metric Learning (DML) aims to learn representation spaces on which semantic relations can simply be expressed through predefined distance metrics. Best performing approaches commonly leverage class proxies as sample stand-ins for better convergence and generalization. However, these proxy-methods solely optimize for sample-proxy distances. Given the inherent non-bijectiveness of used distance functions, this can induce locally isotropic sample distributions, leading to crucial semantic context being missed due to difficulties resolving local structures and intraclass relations between samples. To alleviate this problem, we propose non-isotropy regularization (NIR) for proxy-based Deep Metric Learning. By leveraging Normalizing Flows, we enforce unique translatability of samples from their respective class proxies. This allows us to explicitly induce a non-isotropic distribution of samples around a proxy to optimize for. In doing so, we equip proxy-based objectives to better learn local structures. Extensive experiments highlight consistent generalization benefits of NIR while achieving competitive and state-of-the-art performance on the standard benchmarks CUB200-2011, Cars196 and Stanford Online Products. In addition, we find the superior convergence properties of proxy-based methods to still be retained or even improved, making NIR very attractive for practical usage. Code available at github.com/ExplainableML/NonIsotropicProxyDML. | https://openaccess.thecvf.com/content/CVPR2022/papers/Roth_Non-Isotropy_Regularization_for_Proxy-Based_Deep_Metric_Learning_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.08547 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Non-Isotropy_Regularization_for_Proxy-Based_Deep_Metric_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Roth_Non-Isotropy_Regularization_for_Proxy-Based_Deep_Metric_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
C2AM: Contrastive Learning of Class-Agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation | Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, Linlin Shen | While class activation map (CAM) generated by image classification network has been widely used for weakly supervised object localization (WSOL) and semantic segmentation (WSSS), such classifiers usually focus on discriminative object regions. In this paper, we propose Contrastive learning for Class-agnostic Activation Map (C^2AM) generation only using unlabeled image data, without the involvement of image-level supervision. The core idea comes from the observation that i) semantic information of foreground objects usually differs from their backgrounds; ii) foreground objects with similar appearance or background with similar color/texture have similar representations in the feature space. We form the positive and negative pairs based on the above relations and force the network to disentangle foreground and background with a class-agnostic activation map using a novel contrastive loss. As the network is guided to discriminate cross-image foreground-background, the class-agnostic activation maps learned by our approach generate more complete object regions. We successfully extracted from C^2AM class-agnostic object bounding boxes for object localization and background cues to refine CAM generated by classification network for semantic segmentation. Extensive experiments on CUB-200-2011, ImageNet-1K, and PASCAL VOC2012 datasets show that both WSOL and WSSS can benefit from the proposed C^2AM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_C2AM_Contrastive_Learning_of_Class-Agnostic_Activation_Map_for_Weakly_Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_C2AM_Contrastive_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_C2AM_Contrastive_Learning_of_Class-Agnostic_Activation_Map_for_Weakly_Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_C2AM_Contrastive_Learning_of_Class-Agnostic_Activation_Map_for_Weakly_Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation | Wenqiang Zhang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu, Chunhua Shen | Although vision transformers (ViTs) have achieved great success in computer vision, the heavy computational cost hampers their applications to dense prediction tasks such as semantic segmentation on mobile devices. In this paper, we present a mobile-friendly architecture named Token Pyramid Vision Transformer (TopFormer). The proposed TopFormer takes Tokens from various scales as input to produce scale-aware semantic features, which are then injected into the corresponding tokens to augment the representation. Experimental results demonstrate that our method significantly outperforms CNN- and ViT-based networks across several semantic segmentation datasets and achieves a good trade-off between accuracy and latency. On the ADE20K dataset, TopFormer achieves 5% higher accuracy in mIoU than MobileNetV3 with lower latency on an ARM-based mobile device. Furthermore, the tiny version of TopFormer achieves real-time inference on an ARM-based mobile device with competitive results. The code and models are available at https://github.com/hustvl/TopFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_TopFormer_Token_Pyramid_Transformer_for_Mobile_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_TopFormer_Token_Pyramid_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05525 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_TopFormer_Token_Pyramid_Transformer_for_Mobile_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_TopFormer_Token_Pyramid_Transformer_for_Mobile_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
3DAC: Learning Attribute Compression for Point Clouds | Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo | We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps and different attribute channels, we introduce a deep compression network, termed 3DAC, to explicitly compress the attributes of 3D point clouds and reduce storage usage in this paper. Specifically, the point cloud attributes such as color and reflectance are firstly converted to transform coefficients. We then propose a deep entropy model to model the probabilities of these coefficients by considering information hidden in attribute transforms and previous encoded attributes. Finally, the estimated probabilities are used to further compress these transform coefficients to a final attributes bitstream. Extensive experiments conducted on both indoor and outdoor large-scale open point cloud datasets, including ScanNet and SemanticKITTI, demonstrated the superior compression rates and reconstruction quality of the proposed 3DAC. | https://openaccess.thecvf.com/content/CVPR2022/papers/Fang_3DAC_Learning_Attribute_Compression_for_Point_Clouds_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fang_3DAC_Learning_Attribute_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09931 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_3DAC_Learning_Attribute_Compression_for_Point_Clouds_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Fang_3DAC_Learning_Attribute_Compression_for_Point_Clouds_CVPR_2022_paper.html | CVPR 2022 | null |
Learning a Structured Latent Space for Unsupervised Point Cloud Completion | Yingjie Cai, Kwan-Yee Lin, Chao Zhang, Qiang Wang, Xiaogang Wang, Hongsheng Li | Unsupervised point cloud completion aims at estimating the corresponding complete point cloud of a partial point cloud in an unpaired manner. It is a crucial but challenging problem since there is no paired partial-complete supervision that can be exploited directly. In this work, we propose a novel framework, which learns a unified and structured latent space that encoding both partial and complete point clouds. Specifically, we map a series of related partial point clouds into multiple complete shape and occlusion code pairs and fuse the codes to obtain their representations in the unified latent space. To enforce the learning of such a structured latent space, the proposed method adopts a series of constraints including structured ranking regularization, latent code swapping constraint, and distribution supervision on the related partial point clouds. By establishing such a unified and structured latent space, better partial-complete geometry consistency and shape completion accuracy can be achieved. Extensive experiments show that our proposed method consistently outperforms state-of-the-art unsupervised methods on both synthetic ShapeNet and real-world KITTI, ScanNet, and Matterport3D datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cai_Learning_a_Structured_Latent_Space_for_Unsupervised_Point_Cloud_Completion_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.15580 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_Learning_a_Structured_Latent_Space_for_Unsupervised_Point_Cloud_Completion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cai_Learning_a_Structured_Latent_Space_for_Unsupervised_Point_Cloud_Completion_CVPR_2022_paper.html | CVPR 2022 | null |
The Wanderings of Odysseus in 3D Scenes | Yan Zhang, Siyu Tang | Our goal is to populate digital environments, in which digital humans have diverse body shapes, move perpetually, and have plausible body-scene contact. The core challenge is to generate realistic, controllable, and infinitely long motions for diverse 3D bodies. To this end, we propose generative motion primitives via body surface markers, or GAMMA in short. In our solution, we decompose the long-term motion into a time sequence of motion primitives. We exploit body surface markers and conditional variational autoencoder to model each motion primitive, and generate long-term motion by implementing the generative model recursively. To control the motion to reach a goal, we apply a policy network to explore the generative model's latent space and use a tree-based search to preserve the motion quality during testing. Experiments show that our method can produce more realistic and controllable motion than state-of-the-art data-driven methods. With conventional path-finding algorithms, the generated human bodies can realistically move long distances for a long period of time in the scene. Code is released for research purposes at: https://yz-cnsdqz.github. io/eigenmotion/GAMMA/ | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_The_Wanderings_of_Odysseus_in_3D_Scenes_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_The_Wanderings_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.09251 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_The_Wanderings_of_Odysseus_in_3D_Scenes_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_The_Wanderings_of_Odysseus_in_3D_Scenes_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Learning With Noisy Labels | Kevin J. Liang, Samrudhdhi B. Rangrej, Vladan Petrovic, Tal Hassner | Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be practical, but this problem surprisingly remains largely unexplored. To address mislabeled samples in FSL settings, we make several technical contributions. (1) We offer simple, yet effective, feature aggregation methods, improving the prototypes used by ProtoNet, a popular FSL technique. (2) We describe a novel Transformer model for Noisy Few-Shot Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to weigh mislabeled versus correct samples. (3) Finally, we extensively test these methods on noisy versions of MiniImageNet and TieredImageNet. Our results show that TraNFS is on-par with leading FSL methods on clean support sets, yet outperforms them, by far, in the presence of label noise. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liang_Few-Shot_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_Few-Shot_Learning_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.05494 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Few-Shot_Learning_With_Noisy_Labels_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liang_Few-Shot_Learning_With_Noisy_Labels_CVPR_2022_paper.html | CVPR 2022 | null |
Understanding 3D Object Articulation in Internet Videos | Shengyi Qian, Linyi Jin, Chris Rockwell, Siyi Chen, David F. Fouhey | We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary RGB videos. While seemingly easy for humans, this problem poses many challenges for computers. Our approach is based on a top-down detection system that finds planes that can be articulated. This approach is followed by optimizing for a 3D plane that explains a sequence of detected articulations. We show that this system can be trained on a combination of videos and 3D scan datasets. When tested on a dataset of challenging Internet videos and the Charades dataset, our approach obtains strong performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Qian_Understanding_3D_Object_Articulation_in_Internet_Videos_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_Understanding_3D_Object_Articulation_in_Internet_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Qian_Understanding_3D_Object_Articulation_in_Internet_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Multi-Level Representation Learning With Semantic Alignment for Referring Video Object Segmentation | Dongming Wu, Xingping Dong, Ling Shao, Jianbing Shen | Referring video object segmentation (RVOS) is a challenging language-guided video grounding task, which requires comprehensively understanding the semantic information of both video content and language queries for object prediction. However, existing methods adopt multi-modal fusion at a frame-based spatial granularity. The limitation of visual representation is prone to causing vision-language mismatching and producing poor segmentation results. To address this, we propose a novel multi-level representation learning approach, which explores the inherent structure of the video content to provide a set of discriminative visual embedding, enabling more effective vision-language semantic alignment. Specifically, we embed different visual cues in terms of visual granularity, including multi-frame long-temporal information at video level, intra-frame spatial semantics at frame level, and enhanced object-aware feature prior at object level. With the powerful multi-level visual embedding and carefully-designed dynamic alignment, our model can generate a robust representation for accurate video object segmentation. Extensive experiments on Refer-DAVIS_ 17 and Refer-YouTube-VOS demonstrate that our model achieves superior performance both in segmentation accuracy and inference speed. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Multi-Level_Representation_Learning_With_Semantic_Alignment_for_Referring_Video_Object_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Multi-Level_Representation_Learning_With_Semantic_Alignment_for_Referring_Video_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Multi-Level_Representation_Learning_With_Semantic_Alignment_for_Referring_Video_Object_CVPR_2022_paper.html | CVPR 2022 | null |
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better Than Dot-Product Self-Attention | Tong Yu, Ruslan Khalitov, Lei Cheng, Zhirong Yang | Self-Attention is a widely used building block in neural modeling to mix long-range data elements. Most self-attention neural networks employ pairwise dot-products to specify the attention coefficients. However, these methods require O(N^2) computing cost for sequence length N. Even though some approximation methods have been introduced to relieve the quadratic cost, the performance of the dot-product approach is still bottlenecked by the low-rank constraint in the attention matrix factorization. In this paper, we propose a novel scalable and effective mixing building block called Paramixer. Our method factorizes the interaction matrix into several sparse matrices, where we parameterize the non-zero entries by MLPs with the data elements as input. The overall computing cost of the new building block is as low as O(N \log N). Moreover, all factorizing matrices in Paramixer are full-rank, so it does not suffer from the low-rank bottleneck. We have tested the new method on both synthetic and various real-world long sequential data sets and compared it with several state-of-the-art attention networks. The experimental results show that Paramixer has better performance in most learning tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yu_Paramixer_Parameterizing_Mixing_Links_in_Sparse_Factors_Works_Better_Than_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yu_Paramixer_Parameterizing_Mixing_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.10670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Paramixer_Parameterizing_Mixing_Links_in_Sparse_Factors_Works_Better_Than_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yu_Paramixer_Parameterizing_Mixing_Links_in_Sparse_Factors_Works_Better_Than_CVPR_2022_paper.html | CVPR 2022 | null |
Interactive Image Synthesis With Panoptic Layout Generation | Bo Wang, Tao Wu, Minfeng Zhu, Peng Du | Interactive image synthesis from user-guided input is a challenging task when users wish to control the scene structure of a generated image with ease. Although remarkable progress has been made on layout-based image synthesis approaches, existing methods require high-precision inputs such as accurately placed bounding boxes, which might be constantly violated in an interactive setting. When placement of bounding boxes is subject to perturbation, layout-based models suffer from "missing regions" in the constructed semantic layouts and hence undesirable artifacts in the generated images. In this work, we propose Panoptic Layout Generative Adversarial Network (PLGAN) to address this challenge. The PLGAN employs panoptic theory which distinguishes object categories between "stuff" with amorphous boundaries and "things" with well-defined shapes, such that stuff and instance layouts are constructed through separate branches and later fused into panoptic layouts. In particular, the stuff layouts can take amorphous shapes and fill up the missing regions left out by the instance layouts. We experimentally compare our PLGAN with state-of-the-art layout-based models on the COCO-Stuff, Visual Genome, and Landscape datasets. The advantages of PLGAN are not only visually demonstrated but quantitatively verified in terms of inception score, Frechet inception distance, classification accuracy score, and coverage. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Interactive_Image_Synthesis_With_Panoptic_Layout_Generation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Interactive_Image_Synthesis_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02104 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Interactive_Image_Synthesis_With_Panoptic_Layout_Generation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Interactive_Image_Synthesis_With_Panoptic_Layout_Generation_CVPR_2022_paper.html | CVPR 2022 | null |
Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving | Yi-Nan Chen, Hang Dai, Yong Ding | Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in only feature-level virtual view generation and the estimated depth map is effective in both image-level and feature-level in our framework. We propose a disparity-wise dynamic convolution with dynamic kernels sampled from the disparity feature map to filter the features adaptively from a single image for generating virtual image features, which eases the feature degradation caused by the depth estimation errors. Till submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks 1st on car, pedestrian, and cyclist among the monocular 3D detectors with publications on the KITTI-3D benchmark. Our code will be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Pseudo-Stereo_for_Monocular_3D_Object_Detection_in_Autonomous_Driving_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Pseudo-Stereo_for_Monocular_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.02112 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Pseudo-Stereo_for_Monocular_3D_Object_Detection_in_Autonomous_Driving_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Pseudo-Stereo_for_Monocular_3D_Object_Detection_in_Autonomous_Driving_CVPR_2022_paper.html | CVPR 2022 | null |
All-in-One Image Restoration for Unknown Corruption | Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, Xi Peng | In this paper, we study a challenging problem in image restoration, namely, how to develop an all-in-one method that could recover images from a variety of unknown corruption types and levels. To this end, we propose an All-in-one Image Restoration Network (AirNet) consisting of two neural modules, named Contrastive-Based Degraded Encoder (CBDE) and Degradation-Guided Restoration Network (DGRN). The major advantages of AirNet are two-fold. First, it is an all-in-one solution which could recover various degraded images in one network. Second, AirNet is free from the prior of the corruption types and levels, which just uses the observed corrupted image to perform inference. These two advantages enable AirNet to enjoy better flexibility and higher economy in real world scenarios wherein the priors on the corruptions are hard to know and the degradation will change with space and time. Extensive experimental results show the proposed method outperforms 17 image restoration baselines on four challenging datasets. The code is available at https://github.com/XLearning-SCU/2022-CVPR-AirNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_All-in-One_Image_Restoration_for_Unknown_Corruption_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_All-in-One_Image_Restoration_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_All-in-One_Image_Restoration_for_Unknown_Corruption_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_All-in-One_Image_Restoration_for_Unknown_Corruption_CVPR_2022_paper.html | CVPR 2022 | null |
Syntax-Aware Network for Handwritten Mathematical Expression Recognition | Ye Yuan, Xiao Liu, Wondimu Dikubab, Hui Liu, Zhilong Ji, Zhongqin Wu, Xiang Bai | Handwritten mathematical expression recognition (HMER) is a challenging task that has many potential applications. Recent methods for HMER have achieved outstanding performance with an encoder-decoder architecture. However, these methods adhere to the paradigm that the prediction is made "from one character to another", which inevitably yields prediction errors due to the complicated structures of mathematical expressions or crabbed handwritings. In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network. Specifically, we present a set of grammar rules for converting the LaTeX markup sequence of each expression into a parsing tree; then, we model the markup sequence prediction as a tree traverse process with a deep neural network. In this way, the proposed method can effectively describe the syntax context of expressions, alleviating the structure prediction errors of HMER. Experiments on three benchmark datasets demonstrate that our method achieves better recognition performance than prior arts. To further validate the effectiveness of our method, we create a large-scale dataset consisting of 100k handwritten mathematical expression images acquired from ten thousand writers. The source code, new dataset, and pre-trained models of this work will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yuan_Syntax-Aware_Network_for_Handwritten_Mathematical_Expression_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yuan_Syntax-Aware_Network_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Syntax-Aware_Network_for_Handwritten_Mathematical_Expression_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yuan_Syntax-Aware_Network_for_Handwritten_Mathematical_Expression_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Sketching Without Worrying: Noise-Tolerant Sketch-Based Image Retrieval | Ayan Kumar Bhunia, Subhadeep Koley, Abdullah Faiz Ur Rahman Khilji, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song | Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem (i.e., "I can't sketch") has however proven to be fatal for its widespread adoption. This paper tackles this "fear" head on, and for the first time, proposes an auxiliary module for existing retrieval models that predominantly lets the users sketch without having to worry. We first conducted a pilot study that revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch". We consequently design a stroke subset selector that detects noisy strokes, leaving only those which make a positive contribution towards successful retrieval. Our Reinforcement Learning based formulation quantifies the importance of each stroke present in a given subset, based on the extent to which that stroke contributes to retrieval. When combined with pre-trained retrieval models as a pre-processing module, we achieve a significant gain of 8%-10% over standard baselines and in turn report new state-of-the-art performance. Last but not least, we demonstrate the selector once trained, can also be used in a plug-and-play manner to empower various sketch applications in ways that were not previously possible. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bhunia_Sketching_Without_Worrying_Noise-Tolerant_Sketch-Based_Image_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bhunia_Sketching_Without_Worrying_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14817 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Sketching_Without_Worrying_Noise-Tolerant_Sketch-Based_Image_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bhunia_Sketching_Without_Worrying_Noise-Tolerant_Sketch-Based_Image_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
PUMP: Pyramidal and Uniqueness Matching Priors for Unsupervised Learning of Local Descriptors | Jérome Revaud, Vincent Leroy, Philippe Weinzaepfel, Boris Chidlovskii | Existing approaches for learning local image descriptors have shown remarkable achievements in a wide range of geometric tasks. However, most of them require per-pixel correspondence-level supervision, which is difficult to acquire at scale and in high quality. In this paper, we propose to explicitly integrate two matching priors in a single loss in order to learn local descriptors without supervision. Given two images depicting the same scene, we extract pixel descriptors and build a correlation volume. The first prior enforces the local consistency of matches in this volume via a pyramidal structure iteratively constructed using a non-parametric module. The second prior exploits the fact that each descriptor should match with at most one descriptor from the other image. We combine our unsupervised loss with a standard self-supervised loss trained from synthetic image augmentations. Feature descriptors learned by the proposed approach outperform their fully- and self-supervised counterparts on various geometric benchmarks such as visual localization and image matching, achieving state-of-the-art performance. Project webpage: https://europe.naverlabs.com/research/3d-vision/pump | https://openaccess.thecvf.com/content/CVPR2022/papers/Revaud_PUMP_Pyramidal_and_Uniqueness_Matching_Priors_for_Unsupervised_Learning_of_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Revaud_PUMP_Pyramidal_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Revaud_PUMP_Pyramidal_and_Uniqueness_Matching_Priors_for_Unsupervised_Learning_of_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Revaud_PUMP_Pyramidal_and_Uniqueness_Matching_Priors_for_Unsupervised_Learning_of_CVPR_2022_paper.html | CVPR 2022 | null |
PlanarRecon: Real-Time 3D Plane Detection and Reconstruction From Posed Monocular Videos | Yiming Xie, Matheus Gadelha, Fengting Yang, Xiaowei Zhou, Huaizu Jiang | We present PlanarRecon -- a novel framework for globally coherent detection and reconstruction of 3D planes from a posed monocular video. Unlike previous works that detect planes in 2D from a single image, PlanarRecon incrementally detects planes in 3D for each video fragment, which consists of a set of key frames, from a volumetric representation of the scene using neural networks. A learning-based tracking and fusion module is designed to merge planes from previous fragments to form a coherent global plane reconstruction. Such design allows PlanarRecon to integrate observations from multiple views within each fragment and temporal information across different ones, resulting in an accurate and coherent reconstruction of the scene abstraction with low-polygonal geometry. Experiments show that the proposed approach achieves state-of-the-art performances on the ScanNet dataset while being real-time. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_PlanarRecon_Real-Time_3D_Plane_Detection_and_Reconstruction_From_Posed_Monocular_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_PlanarRecon_Real-Time_3D_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_PlanarRecon_Real-Time_3D_Plane_Detection_and_Reconstruction_From_Posed_Monocular_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_PlanarRecon_Real-Time_3D_Plane_Detection_and_Reconstruction_From_Posed_Monocular_CVPR_2022_paper.html | CVPR 2022 | null |
Deep Equilibrium Optical Flow Estimation | Shaojie Bai, Zhengyang Geng, Yash Savani, J. Zico Kolter | Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a stable flow estimation. However, these RNNs impose large computation and memory overheads, and are not directly trained to model such "stable estimation". They can converge poorly and thereby suffer from performance degradation. To combat these drawbacks, we propose deep equilibrium (DEQ) flow estimators, an approach that directly solves for the flow as the infinite-level fixed point of an implicit layer (using any black-box solver), and differentiates through this fixed point analytically (thus requiring O(1) training memory). This implicit-depth approach is not predicated on any specific model, and thus can be applied to a wide range of SOTA flow estimation model designs (e.g., RAFT and GMA). The use of these DEQ flow estimators allows us to compute the flow faster using, e.g., fixed-point reuse and inexact gradients, consumes 4-6x less training memory than the recurrent counterpart, and achieves better results with the same computation budget. In addition, we propose a novel, sparse fixed-point correction scheme to stabilize our DEQ flow estimators, which addresses a longstanding challenge for DEQ models in general. We test our approach in various realistic settings and show that it improves SOTA methods on Sintel and KITTI datasets with substantially better computational and memory efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Bai_Deep_Equilibrium_Optical_Flow_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Bai_Deep_Equilibrium_Optical_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2204.08442 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Deep_Equilibrium_Optical_Flow_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Bai_Deep_Equilibrium_Optical_Flow_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Optimizing Video Prediction via Video Frame Interpolation | Yue Wu, Qiang Wen, Qifeng Chen | Video prediction is an extrapolation task that predicts future frames given past frames, and video frame interpolation is an interpolation task that estimates intermediate frames between two frames. We have witnessed the tremendous advancement of video frame interpolation, but the general video prediction in the wild is still an open question. Inspired by the photo-realistic results of video frame interpolation, we present a new optimization framework for video prediction via video frame interpolation, in which we solve an extrapolation problem based on an interpolation model. Our video prediction framework is based on optimization with a pretrained differentiable video frame interpolation module without the need for a training dataset, and thus there is no domain gap issue between training and test data. Also, our approach does not need any additional information such as semantic or instance maps, which makes our framework applicable to any video. Extensive experiments on the Cityscapes, KITTI, DAVIS, Middlebury, and Vimeo90K datasets show that our video prediction results are robust in general scenarios, and our approach outperforms other video prediction methods that require a large amount of training data or extra semantic information. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Optimizing_Video_Prediction_via_Video_Frame_Interpolation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Optimizing_Video_Prediction_via_Video_Frame_Interpolation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Optimizing_Video_Prediction_via_Video_Frame_Interpolation_CVPR_2022_paper.html | CVPR 2022 | null |
Motron: Multimodal Probabilistic Human Motion Forecasting | Tim Salzmann, Marco Pavone, Markus Ryll | Autonomous systems and humans are increasingly sharing the same space. Robots work side by side or even hand in hand with humans to balance each other's limitations. Such cooperative interactions are ever more sophisticated. Thus, the ability to reason not just about a human's center of gravity position, but also its granular motion is an important prerequisite for human-robot interaction. Though, many algorithms ignore the multimodal nature of humans or neglect uncertainty in their motion forecasts. We present Motron, a multimodal, probabilistic, graph-structured model, that captures human's multimodality using probabilistic methods while being able to output deterministic maximum-likelihood motions and corresponding confidence values for each mode. Our model aims to be tightly integrated with the robotic planning-control-interaction loop; outputting physically feasible human motions and being computationally efficient. We demonstrate the performance of our model on several challenging real-world motion forecasting datasets, outperforming a wide array of generative/variational methods while providing state-of-the-art single-output motions if required. Both using significantly less computational power than state-of-the art algorithms. | https://openaccess.thecvf.com/content/CVPR2022/papers/Salzmann_Motron_Multimodal_Probabilistic_Human_Motion_Forecasting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Salzmann_Motron_Multimodal_Probabilistic_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.04132 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Salzmann_Motron_Multimodal_Probabilistic_Human_Motion_Forecasting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Salzmann_Motron_Multimodal_Probabilistic_Human_Motion_Forecasting_CVPR_2022_paper.html | CVPR 2022 | null |
Episodic Memory Question Answering | Samyak Datta, Sameer Dharur, Vincent Cartillier, Ruta Desai, Mukul Khanna, Dhruv Batra, Devi Parikh | Egocentric augmented reality devices such as wearable glasses passively capture visual data as a human wearer tours a home environment. We envision a scenario wherein the human communicates with an AI agent powering such a device by asking questions (e.g., "where did you last see my keys?"). In order to succeed at this task, the egocentric AI assistant must (1) construct semantically rich and efficient scene memories that encode spatio-temporal information about objects seen during the tour and (2) possess the ability to understand the question and ground its answer into the semantic memory representation. Towards that end, we introduce (1) a new task -- Episodic Memory Question Answering (EMQA) wherein an egocentric AI assistant is provided with a video sequence (the tour) and a question as an input and is asked to localize its answer to the question within the tour, (2) a dataset of grounded questions designed to probe the agent's spatio-temporal understanding of the tour, and (3) a model for the task that encodes the scene as an allocentric, top-down semantic feature map and grounds the question into the map to localize the answer. We show that our choice of episodic scene memory outperforms naive, off-the-shelf solutions for the task as well as a host of very competitive baselines and is robust to noise in depth, pose as well as camera jitter. | https://openaccess.thecvf.com/content/CVPR2022/papers/Datta_Episodic_Memory_Question_Answering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Datta_Episodic_Memory_Question_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.01652 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Datta_Episodic_Memory_Question_Answering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Datta_Episodic_Memory_Question_Answering_CVPR_2022_paper.html | CVPR 2022 | null |
Continual Stereo Matching of Continuous Driving Scenes With Growing Architecture | Chenghao Zhang, Kun Tian, Bin Fan, Gaofeng Meng, Zhaoxiang Zhang, Chunhong Pan | The deep stereo models have achieved state-of-the-art performance on driving scenes, but they suffer from severe performance degradation when tested on unseen scenes. Although recent work has narrowed this performance gap through continuous online adaptation, this setup requires continuous gradient updates at inference and can hardly deal with rapidly changing scenes. To address these challenges, we propose to perform continual stereo matching where a model is tasked to 1) continually learn new scenes, 2) overcome forgetting previously learned scenes, and 3) continuously predict disparities at deployment. We achieve this goal by introducing a Reusable Architecture Growth (RAG) framework. RAG leverages task-specific neural unit search and architecture growth for continual learning of new scenes. During growth, it can maintain high reusability by reusing previous neural units while achieving good performance. A module named Scene Router is further introduced to adaptively select the scene-specific architecture path at inference. Experimental results demonstrate that our method achieves compelling performance in various types of challenging driving scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Continual_Stereo_Matching_of_Continuous_Driving_Scenes_With_Growing_Architecture_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Continual_Stereo_Matching_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Continual_Stereo_Matching_of_Continuous_Driving_Scenes_With_Growing_Architecture_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Continual_Stereo_Matching_of_Continuous_Driving_Scenes_With_Growing_Architecture_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Backdoor Defense Using Shapley Estimation | Jiyang Guan, Zhuozhuo Tu, Ran He, Dacheng Tao | Deep neural networks have achieved impressive performance in a variety of tasks over the last decade, such as autonomous driving, face recognition, and medical diagnosis. However, prior works show that deep neural networks are easily manipulated into specific, attacker-decided behaviors in the inference stage by backdoor attacks which inject malicious small hidden triggers into model training, raising serious security threats. To determine the triggered neurons and protect against backdoor attacks, we exploit Shapley value and develop a new approach called Shapley Pruning (ShapPruning) that successfully mitigates backdoor attacks from models in a data-insufficient situation (1 image per class or even free of data). Considering the interaction between neurons, ShapPruning identifies the few infected neurons (under 1% of all neurons) and manages to protect the model's structure and accuracy after pruning as many infected neurons as possible. To accelerate ShapPruning, we further propose discarding threshold and epsilon-greedy strategy to accelerate Shapley estimation, making it possible to repair poisoned models with only several minutes. Experiments demonstrate the effectiveness and robustness of our method against various attacks and tasks compared to existing methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Guan_Few-Shot_Backdoor_Defense_Using_Shapley_Estimation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.14889 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Few-Shot_Backdoor_Defense_Using_Shapley_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Guan_Few-Shot_Backdoor_Defense_Using_Shapley_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Cycle-Consistent Counterfactuals by Latent Transformations | Saeed Khorram, Li Fuxin | CounterFactual (CF) visual explanations try to find images similar to the query image that change the decision of a vision system to a specified outcome. Existing methods either require inference-time optimization or joint training with a generative adversarial model which makes them time-consuming and difficult to use in practice. We propose a novel approach, Cycle-Consistent Counterfactuals by Latent Transformations (C3LT), which learns a latent transformation that automatically generates visual CFs by steering in the latent space of generative models. Our method uses cycle consistency between the query and CF latent representations which helps our training to find better solutions. C3LT can be easily plugged into any state-of-the-art pretrained generative network. This enables our method to generate high-quality and interpretable CF images at high resolution such as those in ImageNet. In addition to several established metrics for evaluating CF explanations, we introduce a novel metric tailored to assess the quality of the generated CF examples and validate the effectiveness of our method on an extensive set of experiments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Khorram_Cycle-Consistent_Counterfactuals_by_Latent_Transformations_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Khorram_Cycle-Consistent_Counterfactuals_by_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Khorram_Cycle-Consistent_Counterfactuals_by_Latent_Transformations_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Khorram_Cycle-Consistent_Counterfactuals_by_Latent_Transformations_CVPR_2022_paper.html | CVPR 2022 | null |
ADeLA: Automatic Dense Labeling With Attention for Viewpoint Shift in Semantic Segmentation | Hanxiang Ren, Yanchao Yang, He Wang, Bokui Shen, Qingnan Fan, Youyi Zheng, C. Karen Liu, Leonidas J. Guibas | We describe a method to deal with performance drop in semantic segmentation caused by viewpoint changes within multi-camera systems, where temporally paired images are readily available, but the annotations may only be abundant for a few typical views. Existing methods alleviate performance drop via domain alignment in a shared space and assume that the mapping from the aligned space to the output is transferable. However, the novel content induced by viewpoint changes may nullify such a space for effective alignments, thus resulting in negative adaptation. Our method works without aligning any statistics of the images between the two domains. Instead, it utilizes a novel attention-based view transformation network trained only on color images to hallucinate the semantic images for the target. Despite the lack of supervision, the view transformation network can still generalize to semantic images thanks to the induced "information transport" bias. Furthermore, to resolve ambiguities in converting the semantic images to semantic labels, we treat the view transformation network as a functional representation of an unknown mapping implied by the color images and propose functional label hallucination to generate pseudo-labels with uncertainties in the target domains. Our method surpasses baselines built on state-of-the-art correspondence estimation and view synthesis methods. Moreover, it outperforms the state-of-the-art unsupervised domain adaptation methods that utilize self-training and adversarial domain alignments. Our code and dataset will be made publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ren_ADeLA_Automatic_Dense_Labeling_With_Attention_for_Viewpoint_Shift_in_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ren_ADeLA_Automatic_Dense_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_ADeLA_Automatic_Dense_Labeling_With_Attention_for_Viewpoint_Shift_in_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ren_ADeLA_Automatic_Dense_Labeling_With_Attention_for_Viewpoint_Shift_in_CVPR_2022_paper.html | CVPR 2022 | null |
Joint Hand Motion and Interaction Hotspots Prediction From Egocentric Videos | Shaowei Liu, Subarna Tripathi, Somdeb Majumdar, Xiaolong Wang | We propose to forecast future hand-object interactions given an egocentric video. Instead of predicting action labels or pixels, we directly predict the hand motion trajectory and the future contact points on the next active object (i.e., interaction hotspots). This relatively low-dimensional representation provides a concrete description of future interactions. To tackle this task, we first provide an automatic way to collect trajectory and hotspots labels in large-scale data. We then use this data to train an Object-Centric Transformer (OCT) model for prediction. Our model performs hand and object interaction reasoning via the self-attention mechanism in Transformers. OCT also provides a probabilistic framework to sample the future trajectory and hotspots to handle uncertainty in prediction. We perform experiments on the Epic-Kitchens-55, Epic-Kitchens-100, and EGTEA Gaze+ datasets, and show that OCT significantly outperforms state-of-the-art approaches by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Joint_Hand_Motion_and_Interaction_Hotspots_Prediction_From_Egocentric_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Joint_Hand_Motion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Joint_Hand_Motion_and_Interaction_Hotspots_Prediction_From_Egocentric_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Joint_Hand_Motion_and_Interaction_Hotspots_Prediction_From_Egocentric_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Blind Face Restoration via Integrating Face Shape and Generative Priors | Feida Zhu, Junwei Zhu, Wenqing Chu, Xinyi Zhang, Xiaozhong Ji, Chengjie Wang, Ying Tai | Blind face restoration, which aims to reconstruct high-quality images from low-quality inputs, can benefit many applications. Although existing generative-based methods achieve significant progress in producing high-quality images, they often fail to restore natural face shapes and high-fidelity facial details from severely-degraded inputs. In this work, we propose to integrate shape and generative priors to guide the challenging blind face restoration. Firstly, we set up a shape restoration module to recover reasonable facial geometry with 3D reconstruction. Secondly, a pretrained facial generator is adopted as decoder to generate photo-realistic high-resolution images. To ensure high-fidelity, hierarchical spatial features extracted from the low-quality inputs and rendered 3D images are inserted into the decoder with our proposed Adaptive Feature Fusion Block (AFFB). Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task. The proposed Shape and Generative Prior integrated Network (SGPN) can restore high-quality images with clear face shapes and realistic facial details. Experimental results on synthetic and real-world datasets demonstrate SGPN performs favorably against state-of-the-art blind face restoration methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Blind_Face_Restoration_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Blind_Face_Restoration_via_Integrating_Face_Shape_and_Generative_Priors_CVPR_2022_paper.html | CVPR 2022 | null |
MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video | Jinlu Zhang, Zhigang Tu, Jianyu Yang, Yujin Chen, Junsong Yuan | Recent transformer-based solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn spatio-temporal correlation. We observe that the motions of different joints differ significantly. However, the previous methods cannot efficiently model the solid inter-frame correspondence of each joint, leading to insufficient learning of spatial-temporal correlation. We propose MixSTE (Mixed Spatio-Temporal Encoder), which has a temporal transformer block to separately model the temporal motion of each joint and a spatial transformer block to learn inter-joint spatial correlation. These two blocks are utilized alternately to obtain better spatio-temporal feature encoding. In addition, the network output is extended from the central frame to entire frames of the input video, thereby improving the coherence between the input and output sequences. Extensive experiments are conducted on three benchmarks (Human3.6M, MPI-INF-3DHP, and HumanEva). The results show that our model outperforms the state-of-the-art approach by 10.9% P-MPJPE and 7.6% MPJPE. The code is available at https://github.com/JinluZhang1126/MixSTE. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_MixSTE_Seq2seq_Mixed_Spatio-Temporal_Encoder_for_3D_Human_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_MixSTE_Seq2seq_Mixed_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.00859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_MixSTE_Seq2seq_Mixed_Spatio-Temporal_Encoder_for_3D_Human_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_MixSTE_Seq2seq_Mixed_Spatio-Temporal_Encoder_for_3D_Human_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Safe-Student for Safe Deep Semi-Supervised Learning With Unseen-Class Unlabeled Data | Rundong He, Zhongyi Han, Xiankai Lu, Yilong Yin | Deep semi-supervised learning (SSL) methods aim to take advantage of abundant unlabeled data to improve the algorithm performance. In this paper, we consider the problem of safe SSL scenario where unseen-class instances appear in the unlabeled data. This setting is essential and commonly appears in a variety of real applications. One intuitive solution is removing these unseen-class instances after detecting them during the SSL process. Nevertheless, the performance of unseen-class identification is limited by the small number of labeled data and ignoring the availability of unlabeled data. To take advantage of these unseen-class data and ensure performance, we propose a safe SSL method called SAFE-STUDENT from the teacher-student view. Firstly, a new scoring function called energy-discrepancy (ED) is proposed to help the teacher model improve the security of instances selection. Then, a novel unseen-class label distribution learning mechanism mitigates the unseen-class perturbation by calibrating the unseen-class label distribution. Finally, we propose an iterative optimization strategy to facilitate teacher-student network learning. Extensive studies on several representative datasets show that SAFE-STUDENT remarkably outperforms the state-of-the-art, verifying the feasibility and robustness of our method in the under-explored problem. | https://openaccess.thecvf.com/content/CVPR2022/papers/He_Safe-Student_for_Safe_Deep_Semi-Supervised_Learning_With_Unseen-Class_Unlabeled_Data_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/He_Safe-Student_for_Safe_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/He_Safe-Student_for_Safe_Deep_Semi-Supervised_Learning_With_Unseen-Class_Unlabeled_Data_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/He_Safe-Student_for_Safe_Deep_Semi-Supervised_Learning_With_Unseen-Class_Unlabeled_Data_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Zoom Inside Camera Imaging Pipeline | Chengzhou Tang, Yuqiang Yang, Bing Zeng, Ping Tan, Shuaicheng Liu | Existing single image super-resolution methods are either designed for synthetic data, or for real data but in the RGB-to-RGB or the RAW-to-RGB domain. This paper proposes to zoom an image from RAW to RAW inside the camera imaging pipeline. The RAW-to-RAW domain closes the gap between the ideal and the real degradation models. It also excludes the image signal processing pipeline, which refocuses the model learning onto the super-resolution. To these ends, we design a method that receives a low-resolution RAW as the input and estimates the desired higher-resolution RAW jointly with the degradation model. In our method, two convolutional neural networks are learned to constrain the high-resolution image and the degradation model in lower-dimensional subspaces. This subspace constraint converts the ill-posed SISR problem to a well-posed one. To demonstrate the superiority of the proposed method and the RAW-to-RAW domain, we conduct evaluations on the RealSR and the SR-RAW datasets. The results show that our method performs superiorly over the state-of-the-arts both qualitatively and quantitatively, and it also generalizes well and enables zero-shot transfer across different sensors. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Learning_To_Zoom_Inside_Camera_Imaging_Pipeline_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Learning_To_Zoom_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Learning_To_Zoom_Inside_Camera_Imaging_Pipeline_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Learning_To_Zoom_Inside_Camera_Imaging_Pipeline_CVPR_2022_paper.html | CVPR 2022 | null |
High-Fidelity GAN Inversion for Image Attribute Editing | Tengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang, Qifeng Chen | We present a novel high-fidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved (e.g., background, appearance, and illumination). We first analyze the challenges of high-fidelity GAN inversion from the perspective of lossy data compression. With a low bit-rate latent code, previous works have difficulties in preserving high-fidelity details in reconstructed and edited images. Increasing the size of a latent code can improve the accuracy of GAN inversion but at the cost of inferior editability. To improve image fidelity without compromising editability, we propose a distortion consultation approach that employs a distortion map as a reference for high-fidelity reconstruction. In the distortion consultation inversion (DCI), the distortion map is first projected to a high-rate latent map, which then complements the basic low-rate latent code with more details via consultation fusion. To achieve high-fidelity editing, we propose an adaptive distortion alignment (ADA) module with a self-supervised training scheme, which bridges the gap between the edited and inversion images. Extensive experiments in the face and car domains show a clear improvement in both inversion and editing quality. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_High-Fidelity_GAN_Inversion_for_Image_Attribute_Editing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_High-Fidelity_GAN_Inversion_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2109.06590 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_High-Fidelity_GAN_Inversion_for_Image_Attribute_Editing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_High-Fidelity_GAN_Inversion_for_Image_Attribute_Editing_CVPR_2022_paper.html | CVPR 2022 | null |
RCP: Recurrent Closest Point for Point Cloud | Xiaodong Gu, Chengzhou Tang, Weihao Yuan, Zuozhuo Dai, Siyu Zhu, Ping Tan | 3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to construct the cost volume for estimating accurate 3D flow. However, these methods are limited by the fact that it is difficult to define a search window on point clouds because of the irregular data structure. In this paper, we avoid this irregularity by a simple yet effective method. We decompose the problem into two interlaced stages, where the 3D flows are optimized point-wisely at the first stage and then globally regularized in a recurrent network at the second stage. Therefore, the recurrent network only receives the regular point-wise information as the input. In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task. For 3D scene flow estimation, we make comparisons on the widely used FlyingThings3D and KITTI datasets. For point cloud registration, we follow previous works and evaluate the data pairs with large pose and partially overlapping from ModelNet40. The results show that our method outperforms the previous method and achieves a new state-of-the-art performance on both 3D scene flow estimation and point cloud registration, which demonstrates the superiority of the proposed zero-order method on irregular point cloud data. Our source code is available at https://github.com/gxd1994/RCP. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gu_RCP_Recurrent_Closest_Point_for_Point_Cloud_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_RCP_Recurrent_Closest_Point_for_Point_Cloud_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gu_RCP_Recurrent_Closest_Point_for_Point_Cloud_CVPR_2022_paper.html | CVPR 2022 | null |
gDNA: Towards Generative Detailed Neural Avatars | Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J. Black, Andreas Geiger, Otmar Hilliges | To make 3D human avatars widely available, we must be able to generate a variety of 3D virtual humans with varied identities and shapes in arbitrary poses. This task is challenging due to the diversity of clothed body shapes, their complex articulations, and the resulting rich, yet stochastic geometric detail in clothing. Hence, current methods to represent 3D people do not provide a full generative model of people in clothing. In this paper, we propose a novel method that learns to generate detailed 3D shapes of people in a variety of garments with corresponding skinning weights. Specifically, we devise a multi-subject forward skinning module that is learned from only a few posed, un-rigged scans per subject. To capture the stochastic nature of high-frequency details in garments, we leverage an adversarial loss formulation that encourages the model to capture the underlying statistics. We provide empirical evidence that this leads to realistic generation of local details such as wrinkles. We show that our model is able to generate natural human avatars wearing diverse and detailed clothing. Furthermore, we show that our method can be used on the task of fitting human models to raw scans, outperforming the previous state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_gDNA_Towards_Generative_Detailed_Neural_Avatars_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_gDNA_Towards_Generative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.04123 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_gDNA_Towards_Generative_Detailed_Neural_Avatars_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_gDNA_Towards_Generative_Detailed_Neural_Avatars_CVPR_2022_paper.html | CVPR 2022 | null |
A Dual Weighting Label Assignment Scheme for Object Detection | Shuai Li, Chenhang He, Ruihuang Li, Lei Zhang | Label assignment (LA), which aims to assign each training sample a positive (pos) and a negative (neg) loss weight, plays an important role in object detection. Existing LA methods mostly focus on the design of pos weighting function, while the neg weight is directly derived from the pos weight. Such a mechanism limits the learning capacity of detectors. In this paper, we explore a new weighting paradigm, termed dual weighting (DW), to specify pos and neg weights separately. We first identify the key influential factors of pos/neg weights by analyzing the evaluation metrics in object detection, and then design the pos and neg weighting functions based on them. Specifically, the pos weight of a sample is determined by the consistency degree between its classification and localization scores, while the neg weight is decomposed into two terms: the probability that it is a neg sample and its importance conditioned on being a neg sample. Such a weighting strategy offers greater flexibility to distinguish between important and less important samples, resulting in a more effective object detector. Equipped with the proposed DW method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1xschedule, outperforming other existing LA methods. It consistently improves the baselines on COCO by a large margin under various backbones without bells and whistles. Code is available at https://github.com/strongwolf/DW. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_A_Dual_Weighting_Label_Assignment_Scheme_for_Object_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.09730 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_A_Dual_Weighting_Label_Assignment_Scheme_for_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_A_Dual_Weighting_Label_Assignment_Scheme_for_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
FAM: Visual Explanations for the Feature Representations From Deep Convolutional Networks | Yuxi Wu, Changhuai Chen, Jun Che, Shiliang Pu | In recent years, increasing attention has been drawn to the internal mechanisms of representation models. Traditional methods are inapplicable to fully explain the feature representations, especially if the images do not fit into any category. In this case, employing an existing class or the similarity with other image is unable to provide a complete and reliable visual explanation. To handle this task, we propose a novel visual explanation paradigm called Feature Activation Mapping (FAM) in this paper. Under this paradigm, Grad-FAM and Score-FAM are designed for visualizing feature representations. Unlike the previous approaches, FAM locates the regions of images that contribute most to the feature vector itself. Extensive experiments and evaluations, both subjective and objective, showed that Score-FAM provided most promising interpretable visual explanations for feature representations in Person Re-Identification. Furthermore, FAM also can be employed to analyze other vision tasks, such as self-supervised representation learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_FAM_Visual_Explanations_for_the_Feature_Representations_From_Deep_Convolutional_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_FAM_Visual_Explanations_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_FAM_Visual_Explanations_for_the_Feature_Representations_From_Deep_Convolutional_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_FAM_Visual_Explanations_for_the_Feature_Representations_From_Deep_Convolutional_CVPR_2022_paper.html | CVPR 2022 | null |
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning | Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, Ivan Oseledets | Metric learning aims to learn a highly discriminative model encouraging the embeddings of similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The common recipe is to use an encoder to extract embeddings and a distance-based loss function to match the representations -- usually, the Euclidean distance is utilized. An emerging interest in learning hyperbolic data embeddings suggests that hyperbolic geometry can be beneficial for natural data. Following this line of work, we propose a new hyperbolic-based model for metric learning. At the core of our method is a vision transformer with output embeddings mapped to hyperbolic space. These embeddings are directly optimized using modified pairwise cross-entropy loss. We evaluate the proposed model with six different formulations on four datasets achieving the new state-of-the-art performance. The source code is available at https://github.com/htdt/hyp_metric. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ermolov_Hyperbolic_Vision_Transformers_Combining_Improvements_in_Metric_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ermolov_Hyperbolic_Vision_Transformers_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.10833 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ermolov_Hyperbolic_Vision_Transformers_Combining_Improvements_in_Metric_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ermolov_Hyperbolic_Vision_Transformers_Combining_Improvements_in_Metric_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
MaskGIT: Masked Generative Image Transformer | Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, William T. Freeman | Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative transformer models so far, however, still treat an image naively as a sequence of tokens, and decode an image sequentially following the raster scan ordering (i.e. line-by-line). We find this strategy neither optimal nor efficient. This paper proposes a novel image synthesis paradigm using a bidirectional transformer decoder, which we term MaskGIT. During training, MaskGIT learns to predict randomly masked tokens by attending to tokens in all directions. At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation. Our experiments demonstrate that MaskGIT significantly outperforms the state-of-the-art transformer model on the ImageNet dataset, and accelerates autoregressive decoding by up to 48x. Besides, we illustrate that MaskGIT can be easily extended to various image editing tasks, such as inpainting, extrapolation, and image manipulation. Project page: masked-generative-image-transformer.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chang_MaskGIT_Masked_Generative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.04200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Revisiting the "Video" in Video-Language Understanding | Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, Juan Carlos Niebles | What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the limitations and potential of current video-language benchmarks. We find that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding. We also demonstrate how ATP can improve both video-language dataset and model design. We describe a technique for leveraging ATP to better disentangle dataset subsets with a higher concentration of temporally challenging data, improving benchmarking efficacy for causal and temporal understanding. Further, we show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy. | https://openaccess.thecvf.com/content/CVPR2022/papers/Buch_Revisiting_the_Video_in_Video-Language_Understanding_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Buch_Revisiting_the_Video_in_Video-Language_Understanding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Buch_Revisiting_the_Video_in_Video-Language_Understanding_CVPR_2022_paper.html | CVPR 2022 | null |
Local Texture Estimator for Implicit Representation Function | Jaewon Lee, Kyong Hwan Jin | Recent works with an implicit neural function shed light on representing images in arbitrary resolution. However, a standalone multi-layer perceptron shows limited performance in learning high-frequency components. In this paper, we propose a Local Texture Estimator (LTE), a dominant-frequency estimator for natural images, enabling an implicit function to capture fine details while reconstructing images in a continuous manner. When jointly trained with a deep super-resolution (SR) architecture, LTE is capable of characterizing image textures in 2D Fourier space. We show that an LTE-based neural function achieves favorable performance against existing deep SR methods within an arbitrary-scale factor. Furthermore, we demonstrate that our implementation takes the shortest running time compared to previous works. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lee_Local_Texture_Estimator_for_Implicit_Representation_Function_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2111.08918 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Local_Texture_Estimator_for_Implicit_Representation_Function_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lee_Local_Texture_Estimator_for_Implicit_Representation_Function_CVPR_2022_paper.html | CVPR 2022 | null |
Instance-Aware Dynamic Neural Network Quantization | Zhenhua Liu, Yunhe Wang, Kai Han, Siwei Ma, Wen Gao | Quantization is an effective way to reduce the memory and computational costs of deep neural networks in which the full-precision weights and activations are represented using low-bit values. The bit-width for each layer in most of existing quantization methods is static, i.e., the same for all samples in the given dataset. However, natural images are of huge diversity with abundant content and using such a universal quantization configuration for all samples is not an optimal strategy. In this paper, we present to conduct the low-bit quantization for each image individually, and develop a dynamic quantization scheme for exploring their optimal bit-widths. To this end, a lightweight bit-controller is established and trained jointly with the given neural network to be quantized. During inference, the quantization configuration for an arbitrary image will be determined by the bit-widths generated by the controller, e.g., an image with simple texture will be allocated with lower bits and computational complexity and vice versa. Experimental results conducted on benchmarks demonstrate the effectiveness of the proposed dynamic quantization method for achieving state-of-art performance in terms of accuracy and computational complexity. The code will be available at https://github.com/huawei-noah/Efficient-Computing and https://gitee.com/mindspore/models/tree/master/research/cv/DynamicQuant. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Instance-Aware_Dynamic_Neural_Network_Quantization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Instance-Aware_Dynamic_Neural_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Instance-Aware_Dynamic_Neural_Network_Quantization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Instance-Aware_Dynamic_Neural_Network_Quantization_CVPR_2022_paper.html | CVPR 2022 | null |
When To Prune? A Policy Towards Early Structural Pruning | Maying Shen, Pavlo Molchanov, Hongxu Yin, Jose M. Alvarez | Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooking the heavy computation for training. Recent exploration of pre-training pruning at initialization hints on training cost reduction via pruning, but suffers noticeable performance degradation. We attempt to combine the benefits of both directions and propose a policy that prunes as early as possible during training without hurting performance. Instead of pruning at initialization, our method exploits initial dense training for few epochs to quickly guide the architecture, while constantly evaluating dominant sub-networks via neuron importance ranking. This unveils dominant sub-networks whose structures turn stable, allowing conventional pruning to be pushed earlier into the training. To do this early, we further introduce an Early Pruning Indicator (EPI) that relies on sub-network architectural similarity and quickly triggers pruning when the sub-network's architecture stabilizes. Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise "oracle" grid-search that scans through epochs and requires orders of magnitude more compute. Our method yields 1.4% top-1 accuracy boost over state-of-the-art pruning counterparts, cuts down training cost on GPU by 2.4x, hence offers a new efficiency-accuracy boundary for network pruning during training. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shen_When_To_Prune_A_Policy_Towards_Early_Structural_Pruning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shen_When_To_Prune_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2110.12007 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_When_To_Prune_A_Policy_Towards_Early_Structural_Pruning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shen_When_To_Prune_A_Policy_Towards_Early_Structural_Pruning_CVPR_2022_paper.html | CVPR 2022 | null |
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for Cross-Modal Retrieval | Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, Ji-Rong Wen | Large-scale single-stream pre-training has shown dramatic performance in image-text retrieval. Regrettably, it faces low inference efficiency due to heavy attention layers. Recently, two-stream methods like CLIP and ALIGN with high inference efficiency have also shown promising performance, however, they only consider instance-level alignment between the two streams (thus there is still room for improvement). To overcome these limitations, we propose a novel COllaborative Two-Stream vision-language pre-training model termed COTS for image-text retrieval by enhancing cross-modal interaction. In addition to instance-level alignment via momentum contrastive learning, we leverage two extra levels of cross-modal interactions in our COTS: (1) Token-level interaction -- a masked vision-language modeling (MVLM) learning objective is devised without using a cross-stream network module, where variational autoencoder is imposed on the visual encoder to generate visual tokens for each image. (2) Task-level interaction -- a KL-alignment learning objective is devised between text-to-image and image-to-text retrieval tasks, where the probability distribution per task is computed with the negative queues in momentum contrastive learning. Under a fair comparison setting, our COTS achieves the highest performance among all two-stream methods and comparable performance (but with 10,800x faster in inference) w.r.t. the latest single-stream methods. Importantly, our COTS is also applicable to text-to-video retrieval, yielding new state-of-the-art on the widely-used MSR-VTT dataset. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lu_COTS_Collaborative_Two-Stream_Vision-Language_Pre-Training_Model_for_Cross-Modal_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lu_COTS_Collaborative_Two-Stream_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07441 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_COTS_Collaborative_Two-Stream_Vision-Language_Pre-Training_Model_for_Cross-Modal_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lu_COTS_Collaborative_Two-Stream_Vision-Language_Pre-Training_Model_for_Cross-Modal_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
Degree-of-Linear-Polarization-Based Color Constancy | Taishi Ono, Yuhi Kondo, Legong Sun, Teppei Kurita, Yusuke Moriuchi | Color constancy is an essential function in digital photography and a fundamental process for many computer vision applications. Accordingly, many methods have been proposed, and some recent ones have used deep neural networks to handle more complex scenarios. However, both the traditional and latest methods still impose strict assumptions on their target scenes in explicit or implicit ways. This paper shows that the degree of linear polarization dramatically solves the color constancy problem because it allows us to find achromatic pixels stably. Because we only rely on the physics-based polarization model, we significantly reduce the assumptions compared to existing methods. Furthermore, we captured a wide variety of scenes with ground-truth illuminations for evaluation, and the proposed approach achieved state-of-the-art performance with a low computational cost. Additionally, the proposed method can estimate illumination colors from chromatic pixels and manage multi-illumination scenes. Lastly, the evaluation scenes and codes are publicly available to encourage more development in this field. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ono_Degree-of-Linear-Polarization-Based_Color_Constancy_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ono_Degree-of-Linear-Polarization-Based_Color_Constancy_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ono_Degree-of-Linear-Polarization-Based_Color_Constancy_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ono_Degree-of-Linear-Polarization-Based_Color_Constancy_CVPR_2022_paper.html | CVPR 2022 | null |
A Voxel Graph CNN for Object Classification With Event Cameras | Yongjian Deng, Hao Chen, Hai Liu, Youfu Li | Event cameras attract researchers' attention due to their low power consumption, high dynamic range, and extremely high temporal resolution. Learning models on event-based object classification have recently achieved massive success by accumulating sparse events into dense frames to apply traditional 2D learning methods. Yet, these approaches necessitate heavy-weight models and are with high computational complexity due to the redundant information introduced by the sparse-to-dense conversion, limiting the potential of event cameras on real-life applications. This study aims to address the core problem of balancing accuracy and model complexity for event-based classification models. To this end, we introduce a novel graph representation for event data to exploit their sparsity better and customize a lightweight voxel graph convolutional neural network (EV-VGCNN) for event-based classification. Specifically, (1) using voxel-wise vertices rather than previous point-wise inputs to explicitly exploit regional 2D semantics of event streams while keeping the sparsity; (2) proposing a multi-scale feature relational layer (MFRL) to extract spatial and motion cues from each vertex discriminatively concerning its distances to neighbors. Comprehensive experiments show that our model can advance state-of-the-art classification accuracy with extremely low model complexity (merely 0.84M parameters). | https://openaccess.thecvf.com/content/CVPR2022/papers/Deng_A_Voxel_Graph_CNN_for_Object_Classification_With_Event_Cameras_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Deng_A_Voxel_Graph_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.00216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_A_Voxel_Graph_CNN_for_Object_Classification_With_Event_Cameras_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Deng_A_Voxel_Graph_CNN_for_Object_Classification_With_Event_Cameras_CVPR_2022_paper.html | CVPR 2022 | null |
On the Importance of Asymmetry for Siamese Representation Learning | Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen | Many recent self-supervised frameworks for visual representation learning are based on certain forms of Siamese networks. Such networks are conceptually symmetric with two parallel encoders, but often practically asymmetric as numerous mechanisms are devised to break the symmetry. In this work, we conduct a formal study on the importance of asymmetry by explicitly distinguishing the two encoders within the network -- one produces source encodings and the other targets. Our key insight is keeping a relatively lower variance in target than source generally benefits learning. This is empirically justified by our results from five case studies covering different variance-oriented designs, and is aligned with our preliminary theoretical analysis on the baseline. Moreover, we find the improvements from asymmetric designs generalize well to longer training schedules, multiple other frameworks and newer backbones. Finally, the combined effect of several asymmetric designs achieves a state-of-the-art accuracy on ImageNet linear probing and competitive results on downstream transfer. We hope our exploration will inspire more research in exploiting asymmetry for Siamese representation learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_On_the_Importance_of_Asymmetry_for_Siamese_Representation_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_On_the_Importance_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.00613 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_On_the_Importance_of_Asymmetry_for_Siamese_Representation_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_On_the_Importance_of_Asymmetry_for_Siamese_Representation_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning | MohammadReza Davari, Nader Asadi, Sudhir Mudur, Rahaf Aljundi, Eugene Belilovsky | Continual Learning (CL) research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an abrupt loss of knowledge previously learned by a model when the task, or more broadly the data distribution, being trained on changes. In supervised learning problems this forgetting, resulting from a change in the model's representation, is typically measured or observed by evaluating the decrease in old task performance. However, a model's representation can change without losing knowledge about prior tasks. In this work we consider the concept of representation forgetting, observed by using the difference in performance of an optimal linear classifier before and after a new task is introduced. Using this tool we revisit a number of standard continual learning benchmarks and observe that, through this lens, model representations trained without any explicit control for forgetting often experience small representation forgetting and can sometimes be comparable to methods which explicitly control for forgetting, especially in longer task sequences. We also show that representation forgetting can lead to new insights on the effect of model capacity and loss function used in continual learning. Based on our results, we show that a simple yet competitive approach is to learn representations continually with standard supervised contrastive learning while constructing prototypes of class samples when queried on old samples. | https://openaccess.thecvf.com/content/CVPR2022/papers/Davari_Probing_Representation_Forgetting_in_Supervised_and_Unsupervised_Continual_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Davari_Probing_Representation_Forgetting_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13381 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Davari_Probing_Representation_Forgetting_in_Supervised_and_Unsupervised_Continual_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Davari_Probing_Representation_Forgetting_in_Supervised_and_Unsupervised_Continual_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval | Mengjun Cheng, Yipeng Sun, Longchao Wang, Xiongwei Zhu, Kun Yao, Jie Chen, Guoli Song, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang | Visual appearance is considered to be the most important cue to understand images for cross-modal retrieval, while sometimes the scene text appearing in images can provide valuable information to understand the visual semantics. Most of existing cross-modal retrieval approaches ignore the usage of scene text information and directly adding this information may lead to performance degradation in scene text free scenarios. To address this issue, we propose a full transformer architecture to unify these cross-modal retrieval scenarios in a single Vision and Scene Text Aggregation framework (ViSTA). Specifically, ViSTA utilizes transformer blocks to directly encode image patches and fuse scene text embedding to learn an aggregated visual representation for cross-modal retrieval. To tackle the modality missing problem of scene text, we propose a novel fusion token based transformer aggregation approach to exchange the necessary scene text information only through the fusion token and concentrate on the most important features in each modality. To further strengthen the visual modality, we develop dual contrastive learning losses to embed both image-text pairs and fusion-text pairs into a common cross-modal space. Compared to existing methods, ViSTA enables to aggregate relevant scene text semantics with visual appearance, and hence improve results under both scene text free and scene text aware scenarios. Experimental results show that ViSTA outperforms other methods by at least 8.4% at Recall@1 for scene text aware retrieval task. Compared with state-of-the-art scene text free retrieval methods, ViSTA can achieve better accuracy on Flicker30K and MSCOCO while running at least three times faster during the inference stage, which validates the effectiveness of the proposed framework. | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_ViSTA_Vision_and_Scene_Text_Aggregation_for_Cross-Modal_Retrieval_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.16778 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_ViSTA_Vision_and_Scene_Text_Aggregation_for_Cross-Modal_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_ViSTA_Vision_and_Scene_Text_Aggregation_for_Cross-Modal_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
DenseCLIP: Language-Guided Dense Prediction With Context-Aware Prompting | Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, Jiwen Lu | Recent progress has shown that large-scale pre-training using contrastive image-text pairs can be a promising alternative for high-quality visual representation learning from natural language supervision. Benefiting from a broader source of supervision, this new paradigm exhibits impressive transferability to downstream classification tasks and datasets. However, the problem of transferring the knowledge learned from image-text pairs to more complex dense prediction tasks has barely been visited. In this work, we present a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP. To this end, we convert the original image-text matching problem in CLIP to a pixel-text matching problem and use the pixel-text score maps to guide the learning of dense prediction models. By further using the contextual information from the image to prompt the language model, we are able to facilitate our model to better exploit the pre-trained knowledge. Our method is model-agnostic, which can be applied to arbitrary dense prediction systems and various pre-trained visual backbones including both CLIP models and ImageNet pre-trained models. Extensive experiments demonstrate the superior performance of our methods on semantic segmentation, object detection, and instance segmentation tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rao_DenseCLIP_Language-Guided_Dense_Prediction_With_Context-Aware_Prompting_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Rao_DenseCLIP_Language-Guided_Dense_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01518 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rao_DenseCLIP_Language-Guided_Dense_Prediction_With_Context-Aware_Prompting_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rao_DenseCLIP_Language-Guided_Dense_Prediction_With_Context-Aware_Prompting_CVPR_2022_paper.html | CVPR 2022 | null |
Exploring Effective Data for Surrogate Training Towards Black-Box Attack | Xuxiang Sun, Gong Cheng, Hongda Li, Lei Pei, Junwei Han | Without access to the training data where a black-box victim model is deployed, training a surrogate model for black-box adversarial attack is still a struggle. In terms of data, we mainly identify three key measures for effective surrogate training in this paper. First, we show that leveraging the loss introduced in this paper to enlarge the inter-class similarity makes more sense than enlarging the inter-class diversity like existing methods. Next, unlike the approaches that expand the intra-class diversity in an implicit model-agnostic fashion, we propose a loss function specific to the surrogate model for our generator to enhance the intra-class diversity. Finally, in accordance with the in-depth observations for the methods based on proxy data, we argue that leveraging the proxy data is still an effective way for surrogate training. To this end, we propose a triple-player framework by introducing a discriminator into the traditional data-free framework. In this way, our method can be competitive when there are few semantic overlaps between the scarce proxy data (with the size between 1k and 5k) and the training data. We evaluate our method on a range of victim models and datasets. The extensive results witness the effectiveness of our method. Our source code is available at https://github.com/xuxiangsun/ST-Data. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_Exploring_Effective_Data_for_Surrogate_Training_Towards_Black-Box_Attack_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_Exploring_Effective_Data_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Exploring_Effective_Data_for_Surrogate_Training_Towards_Black-Box_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_Exploring_Effective_Data_for_Surrogate_Training_Towards_Black-Box_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
JRDB-Act: A Large-Scale Dataset for Spatio-Temporal Action, Social Group and Activity Detection | Mahsa Ehsanpour, Fatemeh Saleh, Silvio Savarese, Ian Reid, Hamid Rezatofighi | The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and long-tailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset. In this paper, we introduce JRDB-Act, as an extension of the existing JRDB, which is captured by a social mobile manipulator and reflects a real distribution of human daily-life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labeled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act provides social group annotation, conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group). Each annotated label in JRDB-Act is tagged with the annotators' confidence level which contributes to the development of reliable evaluation strategies. In order to demonstrate how one can effectively utilise such annotations, we develop an end-to-end trainable pipeline to learn and infer these tasks, i.e. individual action and social group detection. The data and the evaluation code will be publicly available at https://jrdb.erc.monash.edu/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ehsanpour_JRDB-Act_A_Large-Scale_Dataset_for_Spatio-Temporal_Action_Social_Group_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ehsanpour_JRDB-Act_A_Large-Scale_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ehsanpour_JRDB-Act_A_Large-Scale_Dataset_for_Spatio-Temporal_Action_Social_Group_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ehsanpour_JRDB-Act_A_Large-Scale_Dataset_for_Spatio-Temporal_Action_Social_Group_and_CVPR_2022_paper.html | CVPR 2022 | null |
AR-NeRF: Unsupervised Learning of Depth and Defocus Effects From Natural Images With Aperture Rendering Neural Radiance Fields | Takuhiro Kaneko | Fully unsupervised 3D representation learning has gained attention owing to its advantages in data collection. A successful approach involves a viewpoint-aware approach that learns an image distribution based on generative models (e.g., generative adversarial networks (GANs)) while generating various view images based on 3D-aware models (e.g., neural radiance fields (NeRFs)). However, they require images with various views for training, and consequently, their application to datasets with few or limited viewpoints remains a challenge. As a complementary approach, an aperture rendering GAN (AR-GAN) that employs a defocus cue was proposed. However, an AR-GAN is a CNN-based model and represents a defocus independently from a viewpoint change despite its high correlation, which is one of the reasons for its performance. As an alternative to an AR-GAN, we propose an aperture rendering NeRF (AR-NeRF), which can utilize viewpoint and defocus cues in a unified manner by representing both factors in a common ray-tracing framework. Moreover, to learn defocus-aware and defocus-independent representations in a disentangled manner, we propose aperture randomized training, for which we learn to generate images while randomizing the aperture size and latent codes independently. During our experiments, we applied AR-NeRF to various natural image datasets, including flower, bird, and face images, the results of which demonstrate the utility of AR-NeRF for unsupervised learning of the depth and defocus effects. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kaneko_AR-NeRF_Unsupervised_Learning_of_Depth_and_Defocus_Effects_From_Natural_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kaneko_AR-NeRF_Unsupervised_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kaneko_AR-NeRF_Unsupervised_Learning_of_Depth_and_Defocus_Effects_From_Natural_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kaneko_AR-NeRF_Unsupervised_Learning_of_Depth_and_Defocus_Effects_From_Natural_CVPR_2022_paper.html | CVPR 2022 | null |
Likert Scoring With Grade Decoupling for Long-Term Action Assessment | Angchi Xu, Ling-An Zeng, Wei-Shi Zheng | Long-term action quality assessment is a task of evaluating how well an action is performed, namely, estimating a quality score from a long video. Intuitively, longterm actions generally involve parts exhibiting different levels of skill, and we call the levels of skill as performance grades. For example, technical highlights and faults may appear in the same long-term action. Hence, the final score should be determined by the comprehensive effect of different grades exhibited in the video. To explore this latent relationship, we design a novel Likert scoring paradigm inspired by the Likert scale in psychometrics, in which we quantify the grades explicitly and generate the final quality score by combining the quantitative values and the corresponding responses estimated from the video, instead of performing direct regression. Moreover, we extract gradespecific features, which will be used to estimate the responses of each grade, through a Transformer decoder architecture with diverse learnable queries. The whole model is named as Grade-decoupling Likert Transformer (GDLT), and we achieve state-of-the-art results on two long-term action assessment datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Likert_Scoring_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Likert_Scoring_With_Grade_Decoupling_for_Long-Term_Action_Assessment_CVPR_2022_paper.html | CVPR 2022 | null |
Many-to-Many Splatting for Efficient Video Frame Interpolation | Ping Hu, Simon Niklaus, Stan Sclaroff, Kate Saenko | Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant. Yet due to the inherent challenges of motion estimation (e.g. occlusions and discontinuities), most state-of-the-art interpolation approaches require subsequent refinement of the warped result to generate satisfying outputs, which drastically decreases the efficiency for multi-frame interpolation. In this work, we propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Specifically, given a frame pair, we estimate multiple bidirectional flows to directly forward warp the pixels to the desired time step, and then fuse any overlapping pixels. In doing so, each source pixel renders multiple target pixels and each target pixel can be synthesized from a larger area of visual context. This establishes a many-to-many splatting scheme with robustness to artifacts like holes. Moreover, for each input frame pair, M2M only performs motion estimation once and has a minuscule computational overhead when interpolating an arbitrary number of in-between frames, hence achieving fast multi-frame interpolation. We conducted extensive experiments to analyze M2M, and found that it significantly improves the efficiency while maintaining high effectiveness. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Many-to-Many_Splatting_for_Efficient_Video_Frame_Interpolation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.03513 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Many-to-Many_Splatting_for_Efficient_Video_Frame_Interpolation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Many-to-Many_Splatting_for_Efficient_Video_Frame_Interpolation_CVPR_2022_paper.html | CVPR 2022 | null |
Investigating Top-k White-Box and Transferable Black-Box Attack | Chaoning Zhang, Philipp Benz, Adil Karjauv, Jae Won Cho, Kang Zhang, In So Kweon | Existing works have identified the limitation of top-1 attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box setting, while our work extends it to a more practical black-box setting: transferable attack. It is widely reported that stronger I-FGSM transfers worse than simple FGSM, leading to a popular belief that transferability is at odds with the white-box attack strength. Our work challenges this belief with empirical finding that stronger attack actually transfers better for the general top-k ASR indicated by the interest class rank (ICR) after attack. For increasing the attack strength, with an intuitive interpretation of the logit gradient from the geometric perspective, we identify that the weakness of the commonly used losses lie in prioritizing the speed to fool the network instead of maximizing its strength. To this end, we propose a new normalized CE loss that guides the logit to be updated in the direction of implicitly maximizing its rank distance from the ground-truth class. Extensive results in various settings have verified that our proposed new loss is simple yet effective for top-k attack. Code is available at: https://bit.ly/3uCiomP | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Investigating_Top-k_White-Box_and_Transferable_Black-Box_Attack_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Investigating_Top-k_White-Box_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Investigating_Top-k_White-Box_and_Transferable_Black-Box_Attack_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Investigating_Top-k_White-Box_and_Transferable_Black-Box_Attack_CVPR_2022_paper.html | CVPR 2022 | null |
Decoupling and Recoupling Spatiotemporal Representation for RGB-D-Based Motion Recognition | Benjia Zhou, Pichao Wang, Jun Wan, Yanyan Liang, Fan Wang, Du Zhang, Zhen Lei, Hao Li, Rong Jin | Decoupling spatiotemporal representation refers to decomposing the spatial and temporal features into dimension-independent factors. Although previous RGB-D-based motion recognition methods have achieved promising performance through the tightly coupled multi-modal spatiotemporal representation, they still suffer from (i) optimization difficulty under small data setting due to the tightly spatiotemporal-entangled modeling; (ii) information redundancy as it usually contains lots of marginal information that is weakly relevant to classification; and (iii) low interaction between multi-modal spatiotemporal information caused by insufficient late fusion. To alleviate these drawbacks, we propose to decouple and recouple spatiotemporal representation for RGB-D-based motion recognition. Specifically, we disentangle the task of learning spatiotemporal representation into 3 sub-tasks: (1) Learning high-quality and dimension independent features through a decoupled spatial and temporal modeling network. (2) Recoupling the decoupled representation to establish stronger space-time dependency. (3) Introducing a Cross-modal Adaptive Posterior Fusion (CAPF) mechanism to capture cross-modal spatiotemporal information from RGB-D data. Seamless combination of these novel designs forms a robust spatiotemporal representation and achieves better performance than state-of-the-art methods on four public motion datasets. Our code is available at https://github.com/damo-cv/MotionRGBD. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_Decoupling_and_Recoupling_Spatiotemporal_Representation_for_RGB-D-Based_Motion_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_Decoupling_and_Recoupling_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Decoupling_and_Recoupling_Spatiotemporal_Representation_for_RGB-D-Based_Motion_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_Decoupling_and_Recoupling_Spatiotemporal_Representation_for_RGB-D-Based_Motion_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Learn by Jointly Optimizing Neural Architecture and Weights | Yadong Ding, Yu Wu, Chengyue Huang, Siliang Tang, Yi Yang, Longhui Wei, Yueting Zhuang, Qi Tian | Meta-learning enables models to adapt to new environments rapidly with a few training examples. Current gradient-based meta-learning methods concentrate on finding good initialization (meta-weights) for learners but ignore the impact of neural architectures. In this paper, we aim to obtain better meta-learners by co-optimizing the architecture and meta-weights simultaneously. Existing NAS-based meta-learning methods apply a two-stage strategy, i.e., first searching architectures and then re-training meta-weights on the searched architecture. However, this two-stage strategy would break the mutual impact of the architecture and meta-weights since they are optimized separately. Differently, we propose progressive connection consolidation, fixing the architecture layer by layer, in which the layer with the largest weight value would be fixed first. In this way, we can jointly search architectures and train the meta-weights on fixed layers. Besides, to improve the generalization performance of the searched meta-learner on all tasks, we propose a more effective rule for co-optimization, namely Connection-Adaptive Meta-learning (CAML). By searching only once, we can obtain both adaptive architecture and meta-weights for meta-learning. Extensive experiments show that our method achieves state-of-the-art performance with 3x less computational cost, revealing our method's effectiveness and efficiency. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Learning_To_Learn_by_Jointly_Optimizing_Neural_Architecture_and_Weights_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Learning_To_Learn_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Learning_To_Learn_by_Jointly_Optimizing_Neural_Architecture_and_Weights_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Learning_To_Learn_by_Jointly_Optimizing_Neural_Architecture_and_Weights_CVPR_2022_paper.html | CVPR 2022 | null |
Attributable Visual Similarity Learning | Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu | This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplainability by mapping each sample to a single point in the embedding space with a distance metric (e.g., Mahalanobis distance, Euclidean distance). Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph and then infer the overall similarity accordingly. Furthermore, we establish a bottom-up similarity construction and top-down similarity inference framework to infer the similarity based on semantic hierarchy consistency. We first identify unreliable higher-level similarity nodes and then correct them using the most coherent adjacent lower-level similarity nodes, which simultaneously preserve traces for similarity attribution. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods and verify the interpretability of our framework. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Attributable_Visual_Similarity_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Attributable_Visual_Similarity_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Attributable_Visual_Similarity_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Attributable_Visual_Similarity_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
A Self-Supervised Descriptor for Image Copy Detection | Ed Pizzi, Sreya Dutta Roy, Sugosh Nagavara Ravindra, Priya Goyal, Matthijs Douze | Image copy detection is an important task for content moderation. We introduce SSCD, a model that builds on a recent self-supervised contrastive training objective. We adapt this method to the copy detection task by changing the architecture and training objective, including a pooling operator from the instance matching literature, and adapting contrastive learning to augmentations that combine images. Our approach relies on an entropy regularization term, promoting consistent separation between descriptor vectors, and we demonstrate that this significantly improves copy detection accuracy. Our method produces a compact descriptor vector, suitable for real-world web scale applications. Statistical information from a background image distribution can be incorporated into the descriptor. On the recent DISC2021 benchmark, SSCD is shown to outperform both baseline copy detection models and self-supervised architectures designed for image classification by huge margins, in all settings. For example, SSCD outperforms SimCLR descriptors by 48% absolute. Code is available at https://github.com/facebookresearch/sscd-copy-detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Pizzi_A_Self-Supervised_Descriptor_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.10261 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Pizzi_A_Self-Supervised_Descriptor_for_Image_Copy_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
DyTox: Transformers for Continual Learning With DYnamic TOken eXpansion | Arthur Douillard, Alexandre Ramé, Guillaume Couairon, Matthieu Cord | Deep network architectures struggle to continually learn new tasks without forgetting the previous tasks. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning. However, existing approaches often require a task identifier at test-time, need complex tuning to balance the growing number of parameters, and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we propose a transformer architecture based on a dedicated encoder/decoder framework. Critically, the encoder and decoder are shared among all tasks. Through a dynamic expansion of special tokens, we specialize each forward of our decoder network on a task distribution. Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion. Moreover, this efficient strategy doesn't need any hyperparameter tuning to control the network's expansion. Our model reaches excellent results on CIFAR100 and state-of-the-art performances on the large-scale ImageNet100 and ImageNet1000 while having less parameters than concurrent dynamic frameworks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Douillard_DyTox_Transformers_for_Continual_Learning_With_DYnamic_TOken_eXpansion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Douillard_DyTox_Transformers_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Douillard_DyTox_Transformers_for_Continual_Learning_With_DYnamic_TOken_eXpansion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Douillard_DyTox_Transformers_for_Continual_Learning_With_DYnamic_TOken_eXpansion_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective | Yuejiang Liu, Riccardo Cadei, Jonas Schweizer, Sherwin Bahmani, Alexandre Alahi | Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under distribution shifts and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant variables, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice mixing datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations; (ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a sparse causal graph; (iii) we introduce a style contrastive loss that not only enforces the structure of style representations but also serves as a self-supervisory signal for test-time refinement on the fly. Experiments on synthetic and real datasets show that our proposed method improves the robustness and reusability of learned motion representations, significantly outperforming prior state-of-the-art motion forecasting models for out-of-distribution generalization and low-shot transfer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Towards_Robust_and_Adaptive_Motion_Forecasting_A_Causal_Representation_Perspective_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Towards_Robust_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.14820 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Robust_and_Adaptive_Motion_Forecasting_A_Causal_Representation_Perspective_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Towards_Robust_and_Adaptive_Motion_Forecasting_A_Causal_Representation_Perspective_CVPR_2022_paper.html | CVPR 2022 | null |
Manifold Learning Benefits GANs | Yao Ni, Piotr Koniusz, Richard Hartley, Richard Nock | In this paper, we improve Generative Adversarial Networks by incorporating a manifold learning step into the discriminator. We consider locality-constrained linear and subspace-based manifolds, and locality-constrained non-linear manifolds. In our design, the manifold learning and coding steps are intertwined with layers of the discriminator, with the goal of attracting intermediate feature representations onto manifolds. We adaptively balance the discrepancy between feature representations and their manifold view, which is a trade-off between denoising on the manifold and refining the manifold. We find that locality-constrained non-linear manifolds outperform linear manifolds due to their non-uniform density and smoothness. We also substantially outperform state-of-the-art baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ni_Manifold_Learning_Benefits_GANs_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ni_Manifold_Learning_Benefits_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.12618 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ni_Manifold_Learning_Benefits_GANs_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ni_Manifold_Learning_Benefits_GANs_CVPR_2022_paper.html | CVPR 2022 | null |
A Keypoint-Based Global Association Network for Lane Detection | Jinsheng Wang, Yinchao Ma, Shaofei Huang, Tianrui Hui, Fei Wang, Chen Qian, Tianzhu Zhang | Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes. Lately, some works propose to formulate lane detection as a keypoint estimation problem to describe the shapes of lane lines more flexibly and gradually group adjacent keypoints belonging to the same lane line in a point-by-point manner, which is inefficient and time-consuming during postprocessing. In this paper, we propose a Global Association Network (GANet) to formulate the lane detection problem from a new perspective, where each keypoint is directly regressed to the starting point of the lane line instead of point-by-point extension. Concretely, the association of keypoints to their belonged lane line is conducted by predicting their offsets to the corresponding starting points of lanes globally without dependence on each other, which could be done in parallel to greatly improve efficiency. In addition, we further propose a Lane-aware Feature Aggregator (LFA), which adaptively captures the local correlations between adjacent keypoints to supplement local information to the global association. Extensive experiments on two popular lane detection benchmarks show that our method outperforms previous methods with F1 score of 79.63% on CULane and 97.71% on Tusimple dataset with high FPS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_A_Keypoint-Based_Global_Association_Network_for_Lane_Detection_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.07335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_A_Keypoint-Based_Global_Association_Network_for_Lane_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_A_Keypoint-Based_Global_Association_Network_for_Lane_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Negative-Aware Attention Framework for Image-Text Matching | Kun Zhang, Zhendong Mao, Quan Wang, Yongdong Zhang | Image-text matching, as a fundamental task, bridges the gap between vision and language. The key of this task is to accurately measure similarity between these two modalities. Prior work measuring this similarity mainly based on matched fragments (i.e., word/region with high relevance), while underestimating or even ignoring the effect of mismatched fragments (i.e., word/region with low relevance), e.g., via a typical LeaklyReLU or ReLU operation that forces negative scores close or exact to zero in attention. This work argues that mismatched textual fragments, which contain rich mismatching clues, are also crucial for image-text matching. We thereby propose a novel Negative-Aware Attention Framework (NAAF), which explicitly exploits both the positive effect of matched fragments and the negative effect of mismatched fragments to jointly infer image-text similarity. NAAF (1) delicately designs an iterative optimization method to maximally mine the mismatched fragments, facilitating more discriminative and robust negative effects, and (2) devises the two-branch matching mechanism to precisely calculate similarity/dissimilarity degrees for matched/mismatched fragments with different masks. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the superior effectiveness of our NAAF, achieving state-of-the-art performance. Code will be released at: https://github.com/CrossmodalGroup/NAAF. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Negative-Aware_Attention_Framework_for_Image-Text_Matching_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Negative-Aware_Attention_Framework_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Negative-Aware_Attention_Framework_for_Image-Text_Matching_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Negative-Aware_Attention_Framework_for_Image-Text_Matching_CVPR_2022_paper.html | CVPR 2022 | null |
Semantic-Aligned Fusion Transformer for One-Shot Object Detection | Yizhou Zhao, Xun Guo, Yan Lu | One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowledge. Yet, their performances are often unsatisfactory. In this paper, we attribute this to inappropriate correlation methods that misalign query-support semantics by overlooking spatial structures and scale variances. Upon analysis, we leverage the attention mechanism and propose a simple but effective architecture named Semantic-aligned Fusion Transformer (SaFT) to resolve these issues. Specifically, we equip SaFT with a vertical fusion module (VFM) for cross-scale semantic enhancement and a horizontal fusion module (HFM) for cross-sample feature fusion. Together, they broaden the vision for each feature point from the support to a whole augmented feature pyramid from the query, facilitating semantic-aligned associations. Extensive experiments on multiple benchmarks demonstrate the superiority of our framework. Without fine-tuning on novel classes, it brings significant performance gains to one-stage baselines, lifting state-of-the-art results to a higher level. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Semantic-Aligned_Fusion_Transformer_for_One-Shot_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Semantic-Aligned_Fusion_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09093 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Semantic-Aligned_Fusion_Transformer_for_One-Shot_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Semantic-Aligned_Fusion_Transformer_for_One-Shot_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning | Matthew Gwilliam, Abhinav Shrivastava | By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised methods for learning image representations have reached impressive results on standard benchmarks. The result has been a crowded field -- many methods with substantially different implementations yield results that seem nearly identical on popular benchmarks, such as linear evaluation on ImageNet. However, a single result does not tell the whole story. In this paper, we compare methods using performance-based benchmarks such as linear evaluation, nearest neighbor classification, and clustering for several different datasets, demonstrating the lack of a clear frontrunner within the current state-of-the-art. In contrast to prior work that performs only supervised vs. unsupervised comparison, we compare several different unsupervised methods against each other. To enrich this comparison, we analyze embeddings with measurements such as uniformity, tolerance, and centered kernel alignment (CKA), and propose two new metrics of our own: nearest neighbor graph similarity and linear prediction overlap. We reveal through our analysis that in isolation, single popular methods should not be treated as though they represent the field as a whole, and that future work ought to consider how to leverage the complimentary nature of these methods. We also leverage CKA to provide a framework to robustly quantify augmentation invariance, and provide a reminder that certain types of invariance will be undesirable for downstream tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gwilliam_Beyond_Supervised_vs._CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gwilliam_Beyond_Supervised_vs._Unsupervised_Representative_Benchmarking_and_Analysis_of_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Few-Shot Incremental Learning for Label-to-Image Translation | Pei Chen, Yangkang Zhang, Zejian Li, Lingyun Sun | Label-to-image translation models generate images from semantic label maps. Existing models depend on large volumes of pixel-level annotated samples. When given new training samples annotated with novel semantic classes, the models should be trained from scratch with both learned and new classes. This hinders their practical applications and motivates us to introduce an incremental learning strategy to the label-to-image translation scenario. In this paper, we introduce a few-shot incremental learning method for label-to-image translation. It learns new classes one by one from a few samples of each class. We propose to adopt semantically-adaptive convolution filters and normalization. When incrementally trained on a novel semantic class, the model only learns a few extra parameters of class-specific modulation. Such design avoids catastrophic forgetting of already-learned semantic classes and enables label-to-image translation of scenes with increasingly rich content. Furthermore, to facilitate few-shot learning, we propose a modulation transfer strategy for better initialization. Extensive experiments show that our method outperforms existing related methods in most cases and achieves zero forgetting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Few-Shot_Incremental_Learning_for_Label-to-Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Few-Shot_Incremental_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Few-Shot_Incremental_Learning_for_Label-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Few-Shot_Incremental_Learning_for_Label-to-Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
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