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Neural Fields As Learnable Kernels for 3D Reconstruction | Francis Williams, Zan Gojcic, Sameh Khamis, Denis Zorin, Joan Bruna, Sanja Fidler, Or Litany | We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art results when reconstructing 3D objects and large scenes from sparse oriented points, and can reconstruct shape categories outside the training set with almost no drop in accuracy. The core insight of our approach is that kernel methods are extremely effective for reconstructing shapes when the chosen kernel has an appropriate inductive bias. We thus factor the problem of shape reconstruction into two parts: (1) a backbone neural network which learns kernel parameters from data, and (2) a kernel ridge regression that fits the input points on-the-fly by solving a simple positive definite linear system using the learned kernel. As a result of this factorization, our reconstruction gains the benefits of data-driven methods under sparse point density while maintaining interpolatory behavior, which converges to the ground truth shape as input sampling density increases. Our experiments demonstrate a strong generalization capability to objects outside the train-set category and scanned scenes. | https://openaccess.thecvf.com/content/CVPR2022/papers/Williams_Neural_Fields_As_Learnable_Kernels_for_3D_Reconstruction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Williams_Neural_Fields_As_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Williams_Neural_Fields_As_Learnable_Kernels_for_3D_Reconstruction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Williams_Neural_Fields_As_Learnable_Kernels_for_3D_Reconstruction_CVPR_2022_paper.html | CVPR 2022 | null |
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic Information | Matthew Kowal, Mennatullah Siam, Md Amirul Islam, Neil D. B. Bruce, Richard P. Wildes, Konstantinos G. Derpanis | Deep spatiotemporal models are used in a variety of computer vision tasks, such as action recognition and video object segmentation. Currently, there is a limited understanding of what information is captured by these models in their intermediate representations. For example, while it has been observed that action recognition algorithms are heavily influenced by visual appearance in single static frames, there is no quantitative methodology for evaluating such static bias in the latent representation compared to bias toward dynamic information (e.g., motion). We tackle this challenge by proposing a novel approach for quantifying the static and dynamic biases of any spatiotemporal model. To show the efficacy of our approach, we analyse two widely studied tasks, action recognition and video object segmentation. Our key findings are threefold: (i) Most examined spatiotemporal models are biased toward static information; although, certain two-stream architectures with cross-connections show a better balance between the static and dynamic information captured. (ii) Some datasets that are commonly assumed to be biased toward dynamics are actually biased toward static information. (iii) Individual units (channels) in an architecture can be biased toward static, dynamic or a combination of the two. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kowal_A_Deeper_Dive_Into_What_Deep_Spatiotemporal_Networks_Encode_Quantifying_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kowal_A_Deeper_Dive_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kowal_A_Deeper_Dive_Into_What_Deep_Spatiotemporal_Networks_Encode_Quantifying_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kowal_A_Deeper_Dive_Into_What_Deep_Spatiotemporal_Networks_Encode_Quantifying_CVPR_2022_paper.html | CVPR 2022 | null |
Detector-Free Weakly Supervised Group Activity Recognition | Dongkeun Kim, Jinsung Lee, Minsu Cho, Suha Kwak | Group activity recognition is the task of understanding the activity conducted by a group of people as a whole in a multi-person video. Existing models for this task are often impractical in that they demand ground-truth bounding box labels of actors even in testing or rely on off-the-shelf object detectors. Motivated by this, we propose a novel model for group activity recognition that depends neither on bounding box labels nor on object detector. Our model based on Transformer localizes and encodes partial contexts of a group activity by leveraging the attention mechanism, and represents a video clip as a set of partial context embeddings. The embedding vectors are then aggregated to form a single group representation that reflects the entire context of an activity while capturing temporal evolution of each partial context. Our method achieves outstanding performance on two benchmarks, Volleyball and NBA datasets, surpassing not only the state of the art trained with the same level of supervision, but also some of existing models relying on stronger supervision. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Detector-Free_Weakly_Supervised_Group_Activity_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Detector-Free_Weakly_Supervised_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.02139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Detector-Free_Weakly_Supervised_Group_Activity_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Detector-Free_Weakly_Supervised_Group_Activity_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
NFormer: Robust Person Re-Identification With Neighbor Transformer | Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, Efstratios Gavves | Person re-identification aims to retrieve persons in highly varying settings across different cameras and scenarios, in which robust and discriminative representation learning is crucial. Most research considers learning representations from single images, ignoring any potential interactions between them. However, due to the high intra-identity variations, ignoring such interactions typically leads to outlier features. To tackle this issue, we propose a Neighbor Transformer Network, or NFormer, which explicitly models interactions across all input images, thus suppressing outlier features and leading to more robust representations overall. As modelling interactions between enormous amount of images is a massive task with lots of dis- tractors, NFormer introduces two novel modules, the Landmark Agent Attention, and the Reciprocal Neighbor Softmax. Specifically, the Landmark Agent Attention efficiently models the relation map between images by a low-rank factorization with a few landmarks in feature space. Moreover, the Reciprocal Neighbor Softmax achieves sparse attention to relevant -rather than all- neighbors only, which alleviates interference of irrelevant representations and further relieves the computational burden. In experiments on four large-scale datasets, NFormer achieves a new state-of-the-art. The code is released at https://github.com/haochenheheda/NFormer. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_NFormer_Robust_Person_Re-Identification_With_Neighbor_Transformer_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_NFormer_Robust_Person_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.09331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_NFormer_Robust_Person_Re-Identification_With_Neighbor_Transformer_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_NFormer_Robust_Person_Re-Identification_With_Neighbor_Transformer_CVPR_2022_paper.html | CVPR 2022 | null |
Joint Forecasting of Panoptic Segmentations With Difference Attention | Colin Graber, Cyril Jazra, Wenjie Luo, Liangyan Gui, Alexander G. Schwing | Forecasting of a representation is important for safe and effective autonomy. For this, panoptic segmentations have been studied as a compelling representation in recent work. However, recent state-of-the-art on panoptic segmentation forecasting suffers from two issues: first, individual object instances are treated independently of each other; second, individual object instance forecasts are merged in a heuristic manner. To address both issues, we study a new panoptic segmentation forecasting model that jointly forecasts all object instances in a scene using a transformer model based on 'difference attention.' It further refines the predictions by taking depth estimates into account. We evaluate the proposed model on the Cityscapes and AIODrive datasets. We find difference attention to be particularly suitable for forecasting because the difference of quantities like locations enables a model to explicitly reason about velocities and acceleration. Because of this, we attain state-of-the-art on panoptic segmentation forecasting metrics. | https://openaccess.thecvf.com/content/CVPR2022/papers/Graber_Joint_Forecasting_of_Panoptic_Segmentations_With_Difference_Attention_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Graber_Joint_Forecasting_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07157 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Graber_Joint_Forecasting_of_Panoptic_Segmentations_With_Difference_Attention_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Graber_Joint_Forecasting_of_Panoptic_Segmentations_With_Difference_Attention_CVPR_2022_paper.html | CVPR 2022 | null |
HairCLIP: Design Your Hair by Text and Reference Image | Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Zhentao Tan, Lu Yuan, Weiming Zhang, Nenghai Yu | Hair editing is an interesting and challenging problem in computer vision and graphics. Many existing methods require well-drawn sketches or masks as conditional inputs for editing, however these interactions are neither straightforward nor efficient. In order to free users from the tedious interaction process, this paper proposes a new hair editing interaction mode, which enables manipulating hair attributes individually or jointly based on the texts or reference images provided by users. For this purpose, we encode the image and text conditions in a shared embedding space and propose a unified hair editing framework by leveraging the powerful image text representation capability of the Contrastive Language-Image Pre-Training (CLIP) model. With the carefully designed network structures and loss functions, our framework can perform high-quality hair editing in a disentangled manner. Extensive experiments demonstrate the superiority of our approach in terms of manipulation accuracy, visual realism of editing results, and irrelevant attribute preservation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wei_HairCLIP_Design_Your_Hair_by_Text_and_Reference_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wei_HairCLIP_Design_Your_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.05142 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_HairCLIP_Design_Your_Hair_by_Text_and_Reference_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wei_HairCLIP_Design_Your_Hair_by_Text_and_Reference_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Imposing Consistency for Optical Flow Estimation | Jisoo Jeong, Jamie Menjay Lin, Fatih Porikli, Nojun Kwak | Imposing consistency through proxy tasks has been shown to enhance data-driven learning and enable self-supervision in various tasks. This paper introduces novel and effective consistency strategies for optical flow estimation, a problem where labels from real-world data are very challenging to derive. More specifically, we propose occlusion consistency and zero forcing in the forms of self-supervised learning and transformation consistency in the form of semi-supervised learning. We apply these consistency techniques in a way that the network model learns to describe pixel-level motions better while requiring no additional annotations. We demonstrate that our consistency strategies applied to a strong baseline network model using the original datasets and labels provide further improvements, attaining the state-of-the-art results on the KITTI-2015 scene flow benchmark in the non-stereo category. Our method achieves the best foreground accuracy (4.33% in Fl-all) over both the stereo and non-stereo categories, even though using only monocular image inputs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jeong_Imposing_Consistency_for_Optical_Flow_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jeong_Imposing_Consistency_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07262 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jeong_Imposing_Consistency_for_Optical_Flow_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jeong_Imposing_Consistency_for_Optical_Flow_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Style Transformer for Image Inversion and Editing | Xueqi Hu, Qiusheng Huang, Zhengyi Shi, Siyuan Li, Changxin Gao, Li Sun, Qingli Li | Existing GAN inversion methods fail to provide codes for reliable reconstruction and flexible editing simultaneously. This paper presents a transformer-based image inversion and editing model for pretrained StyleGAN which is not only with less distortions, but also of high quality and flexibility for editing. The proposed model employs a CNN encoder to provide multi-scale image features as keys and values. Meanwhile it regards the style code to be determined for different layers of the generator as queries. It first initializes query tokens as learnable parameters and maps them into W+ space. Then the multi-stage alternate self- and cross-attention are utilized, updating queries with the purpose of inverting the input by the generator. Moreover, based on the inverted code, we investigate the reference- and label-based attribute editing through a pretrained latent classifier, and achieve flexible image-to-image translation with high quality results. Extensive experiments are carried out, showing better performances on both inversion and editing tasks within StyleGAN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Style_Transformer_for_Image_Inversion_and_Editing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hu_Style_Transformer_for_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Style_Transformer_for_Image_Inversion_and_Editing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Style_Transformer_for_Image_Inversion_and_Editing_CVPR_2022_paper.html | CVPR 2022 | null |
OakInk: A Large-Scale Knowledge Repository for Understanding Hand-Object Interaction | Lixin Yang, Kailin Li, Xinyu Zhan, Fei Wu, Anran Xu, Liu Liu, Cewu Lu | Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's interactions based on the affordances. Even though these two knowledge bases are crucial, we find that current databases lack a comprehensive awareness of them. In this work, we propose a multi-modal and rich-annotated knowledge repository, OakInk, for visual and cognitive understanding of hand-object interactions. We start to collect 1,800 common household objects and annotate their affordances to construct the first knowledge base: Oak. Given the affordance, we record rich human interactions with 100 selected objects in Oak. Finally, we transfer the interactions on the 100 recorded objects to their virtual counterparts through a novel method: Tink. The recorded and transferred hand-object interactions constitute the second knowledge base: Ink. As a result, OakInk contains 50,000 distinct affordance-aware and intent-oriented hand-object interactions. We benchmark OakInk on pose estimation and grasp generation tasks. Moreover, we propose two practical applications of OakInk: intent-based interaction generation and handover generation. Our dataset and source code are publicly available at www.oakink.net. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Yang_OakInk_A_Large-Scale_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15709 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.html | CVPR 2022 | null |
Pyramid Adversarial Training Improves ViT Performance | Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu, Dilip Krishnan, Deqing Sun | Aggressive data augmentation is a key component of the strong generalization capabilities of Vision Transformer (ViT). One such data augmentation technique is adversarial training (AT); however, many prior works have shown that this often results in poor clean accuracy. In this work, we present pyramid adversarial training (PyramidAT), a simple and effective technique to improve ViT's overall performance. We pair it with a "matched" Dropout and stochastic depth regularization, which adopts the same Dropout and stochastic depth configuration for the cleanand adversarial samples. Similar to the improvements on CNNs by AdvProp (not directly applicable to ViT), our pyramid adversarial training breaks the trade-off between in-distribution accuracy and out-of-distribution robustness for ViT and related architectures. It leads to 1.82% absolute improvement on ImageNet clean accuracy for the ViT-B model when trained only on ImageNet-1K data, while simultaneously boosting performance on 7 ImageNet robustness metrics, by absolute numbers ranging from 1.76% to 15.68%. We set a new state-of-the-art for ImageNet-C (41.42 mCE), ImageNet-R (53.92%), and ImageNet-Sketch (41.04%) without extra data, using only the ViT-B/16 backbone and our pyramid adversarial training. Our code is publicly available at pyramidat.github.io. | https://openaccess.thecvf.com/content/CVPR2022/papers/Herrmann_Pyramid_Adversarial_Training_Improves_ViT_Performance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Herrmann_Pyramid_Adversarial_Training_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Herrmann_Pyramid_Adversarial_Training_Improves_ViT_Performance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Herrmann_Pyramid_Adversarial_Training_Improves_ViT_Performance_CVPR_2022_paper.html | CVPR 2022 | null |
Bridging Global Context Interactions for High-Fidelity Image Completion | Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai, Dinh Phung | Bridging global context interactions correctly is important for high-fidelity image completion with large masks. Previous methods attempting this via deep or large receptive field (RF) convolutions cannot escape from the dominance of nearby interactions, which may be inferior. In this paper, we propose to treat image completion as a directionless sequence-to-sequence prediction task, and deploy a transformer to directly capture long-range dependence. Crucially, we employ a restrictive CNN with small and non-overlapping RF for weighted token representation, which allows the transformer to explicitly model the long-range visible context relations with equal importance in all layers, without implicitly confounding neighboring tokens when larger RFs are used. To improve appearance consistency between visible and generated regions, a novel attention-aware layer (AAL) is introduced to better exploit distantly related high-frequency features. Overall, extensive experiments demonstrate superior performance compared to state-of-the-art methods on several datasets. Code is available at https://github.com/lyndonzheng/TFill. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Bridging_Global_Context_Interactions_for_High-Fidelity_Image_Completion_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_Bridging_Global_Context_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.00845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Bridging_Global_Context_Interactions_for_High-Fidelity_Image_Completion_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Bridging_Global_Context_Interactions_for_High-Fidelity_Image_Completion_CVPR_2022_paper.html | CVPR 2022 | null |
SwinBERT: End-to-End Transformers With Sparse Attention for Video Captioning | Kevin Lin, Linjie Li, Chung-Ching Lin, Faisal Ahmed, Zhe Gan, Zicheng Liu, Yumao Lu, Lijuan Wang | The canonical approach to video captioning dictates a caption generation model to learn from offline-extracted dense video features. These feature extractors usually operate on video frames sampled at a fixed frame rate and are often trained on image/video understanding tasks, without adaption to video captioning data. In this work, we present SwinBERT, an end-to-end transformer-based model for video captioning, which takes video frame patches directly as inputs, and outputs a natural language description. Instead of leveraging multiple 2D/3D feature extractors, our method adopts a video transformer to encode spatial-temporal representations that can adapt to variable lengths of video input without dedicated design for different frame rates. Based on this model architecture, we show that video captioning can benefit significantly from more densely sampled video frames as opposed to previous successes with sparsely sampled video frames for video-and-language understanding tasks (e.g., video question answering). Moreover, to avoid the inherent redundancy in consecutive video frames, we propose adaptively learning a sparse attention mask and optimizing it for task-specific performance improvement through better long-range video sequence modeling. Through extensive experiments on 5 video captioning datasets, we show that SwinBERT achieves across-the-board performance improvements over previous methods, often by a large margin. The learned sparse attention masks in addition push the limit to new state of the arts, and can be transferred between different video lengths and between different datasets. Code is available at https://github.com/microsoft/SwinBERT | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_SwinBERT_End-to-End_Transformers_With_Sparse_Attention_for_Video_Captioning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_SwinBERT_End-to-End_Transformers_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.13196 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_SwinBERT_End-to-End_Transformers_With_Sparse_Attention_for_Video_Captioning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_SwinBERT_End-to-End_Transformers_With_Sparse_Attention_for_Video_Captioning_CVPR_2022_paper.html | CVPR 2022 | null |
Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation | Yanwu Xu, Shaoan Xie, Wenhao Wu, Kun Zhang, Mingming Gong, Kayhan Batmanghelich | Unpaired image-to-image translation (I2I) is an ill-posed problem, as an infinite number of translation functions can map the source domain distribution to the target distribution. Therefore, much effort has been put into designing suitable constraints, e.g., cycle consistency (CycleGAN), geometry consistency (GCGAN), and contrastive learning-based constraints (CUTGAN), that help better pose the problem. However, these well-known constraints have limitations: (1) they are either too restrictive or too weak for specific I2I tasks; (2) these methods result in content distortion when there is a significant spatial variation between the source and target domains. This paper proposes a universal regularization technique called maximum spatial perturbation consistency (MSPC), which enforces a spatial perturbation function (T) and the translation operator (G) to be commutative (i.e., T \circ G = G \circ T ). In addition, we introduce two adversarial training components for learning the spatial perturbation function. The first one lets T compete with G to achieve maximum perturbation. The second one lets G and T compete with discriminators to align the spatial variations caused by the change of object size, object distortion, background interruptions, etc. Our method outperforms the state-of-the-art methods on most I2I benchmarks. We also introduce a new benchmark, namely the front face to profile face dataset, to emphasize the underlying challenges of I2I for real-world applications. We finally perform ablation experiments to study the sensitivity of our method to the severity of spatial perturbation and its effectiveness for distribution alignment. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Maximum_Spatial_Perturbation_Consistency_for_Unpaired_Image-to-Image_Translation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_Maximum_Spatial_Perturbation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12707 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Maximum_Spatial_Perturbation_Consistency_for_Unpaired_Image-to-Image_Translation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Maximum_Spatial_Perturbation_Consistency_for_Unpaired_Image-to-Image_Translation_CVPR_2022_paper.html | CVPR 2022 | null |
Unseen Classes at a Later Time? No Problem | Hari Chandana Kuchibhotla, Sumitra S Malagi, Shivam Chandhok, Vineeth N Balasubramanian | Recent progress towards learning from limited supervision has encouraged efforts towards designing models that can recognize novel classes at test time (generalized zero-shot learning or GZSL). GZSL approaches assume knowledge of all classes, with or without labeled data, before-hand. However, practical scenarios demand models that are adaptable and can handle dynamic addition of new seen and unseen classes on the fly (i.e continual generalized zero-shot learning or CGZSL). One solution is to sequentially retrain and reuse conventional GZSL methods, however, such an approach suffers from catastrophic forgetting leading to suboptimal generalization performance. A few recent efforts towards tackling CGZSL have been limited by difference in settings, practicality, data splits and protocols followed - inhibiting fair comparison and a clear direction forward. Motivated from these observations, in this work, we firstly consolidate the different CGZSL setting variants and propose a new Online CGZSL setting which is more practical and flexible. Secondly, we introduce a unified feature-generative framework for CGZSL that leverages bi-directional incremental alignment to dynamically adapt to addition of new classes with or without labeled data that arrive over time in any of these CGZSL settings. Our comprehensive experiments and analysis on five benchmark datasets and comparison with baselines show that our approach consistently outperforms existing methods, especially on the more practical Online setting. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kuchibhotla_Unseen_Classes_at_a_Later_Time_No_Problem_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kuchibhotla_Unseen_Classes_at_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16517 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kuchibhotla_Unseen_Classes_at_a_Later_Time_No_Problem_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kuchibhotla_Unseen_Classes_at_a_Later_Time_No_Problem_CVPR_2022_paper.html | CVPR 2022 | null |
InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering | Mijeong Kim, Seonguk Seo, Bohyung Han | We present an information-theoretic regularization technique for few-shot novel view synthesis based on neural implicit representation. The proposed approach minimizes potential reconstruction inconsistency that happens due to insufficient viewpoints by imposing the entropy constraint of the density in each ray. In addition, to alleviate the potential degenerate issue when all training images are acquired from almost redundant viewpoints, we further incorporate the spatially smoothness constraint into the estimated images by restricting information gains from a pair of rays with slightly different viewpoints. The main idea of our algorithm is to make reconstructed scenes compact along individual rays and consistent across rays in the neighborhood. The proposed regularizers can be plugged into most of existing neural volume rendering techniques based on NeRF in a straightforward way. Despite its simplicity, we achieve consistently improved performance compared to existing neural view synthesis methods by large margins on multiple standard benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_InfoNeRF_Ray_Entropy_Minimization_for_Few-Shot_Neural_Volume_Rendering_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_InfoNeRF_Ray_Entropy_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.15399 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_InfoNeRF_Ray_Entropy_Minimization_for_Few-Shot_Neural_Volume_Rendering_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_InfoNeRF_Ray_Entropy_Minimization_for_Few-Shot_Neural_Volume_Rendering_CVPR_2022_paper.html | CVPR 2022 | null |
Dist-PU: Positive-Unlabeled Learning From a Label Distribution Perspective | Yunrui Zhao, Qianqian Xu, Yangbangyan Jiang, Peisong Wen, Qingming Huang | Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhao_Dist-PU_Positive-Unlabeled_Learning_From_a_Label_Distribution_Perspective_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhao_Dist-PU_Positive-Unlabeled_Learning_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Dist-PU_Positive-Unlabeled_Learning_From_a_Label_Distribution_Perspective_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhao_Dist-PU_Positive-Unlabeled_Learning_From_a_Label_Distribution_Perspective_CVPR_2022_paper.html | CVPR 2022 | null |
SC2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration | Zhi Chen, Kun Sun, Fan Yang, Wenbing Tao | In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2) measure to compute the similarity between correspondences. It considers the global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at early stage. Based on this measure, our registration pipeline employs a global spectral technique to find some reliable seeds from the initial correspondences. Then we design a two-stage strategy to expand each seed to a consensus set based on the SC^2 measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm to generate a candidate rigid transformation and select the best model as the final result. Our method can guarantee to find a certain number of outlier-free consensus sets using fewer samplings, making the model estimation more efficient and robust. In addition, the proposed SC^2 measure is general and can be easily plugged into deep learning based frameworks. Extensive experiments are carried out to investigate the performance of our method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_SC2-PCR_A_Second_Order_Spatial_Compatibility_for_Efficient_and_Robust_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_SC2-PCR_A_Second_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_SC2-PCR_A_Second_Order_Spatial_Compatibility_for_Efficient_and_Robust_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_SC2-PCR_A_Second_Order_Spatial_Compatibility_for_Efficient_and_Robust_CVPR_2022_paper.html | CVPR 2022 | null |
Relative Pose From a Calibrated and an Uncalibrated Smartphone Image | Yaqing Ding, Daniel Barath, Jian Yang, Zuzana Kukelova | In this paper, we propose a new minimal and a non-minimal solver for estimating the relative camera pose together with the unknown focal length of the second camera. This configuration has a number of practical benefits, e.g., when processing large-scale datasets. Moreover, it is resistant to the typical degenerate cases of the traditional six-point algorithm. The minimal solver requires four point correspondences and exploits the gravity direction that the built-in IMU of recent smart devices recover. We also propose a linear solver that enables estimating the pose from a larger-than-minimal sample extremely efficiently which then can be improved by, e.g., bundle adjustment. The methods are tested on 35654 image pairs from publicly available real-world datasets and the authors collected datasets. When combined with a recent robust estimator, they lead to results superior to the traditional solvers in terms of rotation, translation and focal length accuracy, while being notably faster. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Relative_Pose_From_a_Calibrated_and_an_Uncalibrated_Smartphone_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ding_Relative_Pose_From_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Relative_Pose_From_a_Calibrated_and_an_Uncalibrated_Smartphone_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Relative_Pose_From_a_Calibrated_and_an_Uncalibrated_Smartphone_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Robust and Reproducible Active Learning Using Neural Networks | Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, Shadab Khan | Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling entire data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we demonstrate that under identical experimental conditions, different types of AL algorithms (uncertainty based, diversity based, and committee based) produce an inconsistent gain over random sampling baseline. Through a variety of experiments, controlling for sources of stochasticity, we show that variance in performance metrics achieved by AL algorithms can lead to results that are not consistent with the previously published results. We also found that under strong regularization, AL methods evaluated led to marginal or no advantage over the random sampling baseline under a variety of experimental conditions. Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions. We share our codes to facilitate AL experimentation. We believe our findings and recommendations will help advance reproducible research in AL using neural networks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Munjal_Towards_Robust_and_Reproducible_Active_Learning_Using_Neural_Networks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Munjal_Towards_Robust_and_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2002.09564 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Munjal_Towards_Robust_and_Reproducible_Active_Learning_Using_Neural_Networks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Munjal_Towards_Robust_and_Reproducible_Active_Learning_Using_Neural_Networks_CVPR_2022_paper.html | CVPR 2022 | null |
Retrieval Augmented Classification for Long-Tail Visual Recognition | Alexander Long, Wei Yin, Thalaiyasingam Ajanthan, Vu Nguyen, Pulak Purkait, Ravi Garg, Alan Blair, Chunhua Shen, Anton van den Hengel | We introduce Retrieval Augmented Classification (RAC), a generic approach to augmenting standard image classification pipelines with an explicit retrieval module. RAC consists of a standard base image encoder fused with a parallel retrieval branch that queries a non-parametric external memory of pre-encoded images and associated text snippets. We apply RAC to the problem of long-tail classification and demonstrate a significant improvement over previous state-of-the-art on Places365-LT and iNaturalist-2018 (14.5% and 6.7% respectively), despite using only the training datasets themselves as the external information source. We demonstrate that RAC's retrieval module, without prompting, learns a high level of accuracy on tail classes. This, in turn, frees the base encoder to focus on common classes, and improve its performance thereon. RAC represents an alternative approach to utilizing large, pretrained models without requiring fine-tuning, as well as a first step towards more effectively making use of external memory within common computer vision architectures. | https://openaccess.thecvf.com/content/CVPR2022/papers/Long_Retrieval_Augmented_Classification_for_Long-Tail_Visual_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Long_Retrieval_Augmented_Classification_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2202.11233 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Long_Retrieval_Augmented_Classification_for_Long-Tail_Visual_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Long_Retrieval_Augmented_Classification_for_Long-Tail_Visual_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Not All Tokens Are Equal: Human-Centric Visual Analysis via Token Clustering Transformer | Wang Zeng, Sheng Jin, Wentao Liu, Chen Qian, Ping Luo, Wanli Ouyang, Xiaogang Wang | Vision transformers have achieved great successes in many computer vision tasks. Most methods generate vision tokens by splitting an image into a regular and fixed grid and treating each cell as a token. However, not all regions are equally important in human-centric vision tasks, e.g., the human body needs a fine representation with many tokens, while the image background can be modeled by a few tokens. To address this problem, we propose a novel Vision Transformer, called Token Clustering Transformer (TCFormer), which merges tokens by progressive clustering, where the tokens can be merged from different locations with flexible shapes and sizes. The tokens in TCFormer can not only focus on important areas but also adjust the token shapes to fit the semantic concept and adopt a fine resolution for regions containing critical details, which is beneficial to capturing detailed information. Extensive experiments show that TCFormer consistently outperforms its counterparts on different challenging humancentric tasks and datasets, including whole-body pose estimation on COCO-WholeBody and 3D human mesh reconstruction on 3DPW. Code is available at https://github.com/ zengwang430521/TCFormer.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zeng_Not_All_Tokens_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08680 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.html | CVPR 2022 | null |
Temporally Efficient Vision Transformer for Video Instance Segmentation | Shusheng Yang, Xinggang Wang, Yu Li, Yuxin Fang, Jiemin Fang, Wenyu Liu, Xun Zhao, Ying Shan | Recently vision transformer has achieved tremendous success on image-level visual recognition tasks. To effectively and efficiently model the crucial temporal information within a video clip, we propose a Temporally Efficient Vision Transformer (TeViT) for video instance segmentation (VIS). Different from previous transformer-based VIS methods, TeViT is nearly convolution-free, which contains a transformer backbone and a query-based video instance segmentation head. In the backbone stage, we propose a nearly parameter-free messenger shift mechanism for early temporal context fusion. In the head stages, we propose a parameter-shared spatiotemporal query interaction mechanism to build the one-to-one correspondence between video instances and queries. Thus, TeViT fully utilizes both frame-level and instance-level temporal context information and obtains strong temporal modeling capacity with negligible extra computational cost. On three widely adopted VIS benchmarks, i.e., YouTube-VIS-2019, YouTube-VIS-2021, and OVIS, TeViT obtains state-of-the-art results and maintains high inference speed, e.g., 46.6 AP with 68.9 FPS on YouTube-VIS-2019. Code is available at https:// github.com/hustvl/TeViT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Yang_Temporally_Efficient_Vision_Transformer_for_Video_Instance_Segmentation_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.08412 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Temporally_Efficient_Vision_Transformer_for_Video_Instance_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Yang_Temporally_Efficient_Vision_Transformer_for_Video_Instance_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
The Devil Is in the Margin: Margin-Based Label Smoothing for Network Calibration | Bingyuan Liu, Ismail Ben Ayed, Adrian Galdran, Jose Dolz | In spite of the dominant performances of deep neural networks, recent works have shown that they are poorly calibrated, resulting in over-confident predictions. Miscalibration can be exacerbated by overfitting due to the minimization of the cross-entropy during training, as it promotes the predicted softmax probabilities to match the one-hot label assignments. This yields a pre-softmax activation of the correct class that is significantly larger than the remaining activations. Recent evidence from the literature suggests that loss functions that embed implicit or explicit maximization of the entropy of predictions yield state-of-the-art calibration performances. We provide a unifying constrained-optimization perspective of current state-of-the-art calibration losses. Specifically, these losses could be viewed as approximations of a linear penalty (or a Lagrangian term) imposing equality constraints on logit distances. This points to an important limitation of such underlying equality constraints, whose ensuing gradients constantly push towards a non-informative solution, which might prevent from reaching the best compromise between the discriminative performance and calibration of the model during gradient-based optimization. Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances. Comprehensive experiments on a variety of image classification, semantic segmentation and NLP benchmarks demonstrate that our method sets novel state-of-the-art results on these tasks in terms of network calibration, without affecting the discriminative performance. The code is available at https://github.com/by-liu/MbLS. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_The_Devil_Is_in_the_Margin_Margin-Based_Label_Smoothing_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_The_Devil_Is_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.15430 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_The_Devil_Is_in_the_Margin_Margin-Based_Label_Smoothing_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_The_Devil_Is_in_the_Margin_Margin-Based_Label_Smoothing_for_CVPR_2022_paper.html | CVPR 2022 | null |
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language Tasks | Fawaz Sammani, Tanmoy Mukherjee, Nikos Deligiannis | Natural language explanation (NLE) models aim at explaining the decision-making process of a black box system via generating natural language sentences which are human-friendly, high-level and fine-grained. Current NLE models explain the decision-making process of a vision or vision-language model (a.k.a., task model), e.g., a VQA model, via a language model (a.k.a., explanation model), e.g., GPT. Other than the additional memory resources and inference time required by the task model, the task and explanation models are completely independent, which disassociates the explanation from the reasoning process made to predict the answer. We introduce NLX-GPT, a general, compact and faithful language model that can simultaneously predict an answer and explain it. We first conduct pre-training on large scale data of image-caption pairs for general understanding of images, and then formulate the answer as a text prediction task along with the explanation. Without region proposals nor a task model, our resulting overall framework attains better evaluation scores, contains much less parameters and is 15x faster than the current SoA model. We then address the problem of evaluating the explanations which can be in many times generic, data-biased and can come in several forms. We therefore design 2 new evaluation measures: (1) explain-predict and (2) retrieval-based attack, a self-evaluation framework that requires no labels. Code is at: https://github.com/cvpr2022annonymous/nlxgpt | https://openaccess.thecvf.com/content/CVPR2022/papers/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sammani_NLX-GPT_A_Model_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sammani_NLX-GPT_A_Model_for_Natural_Language_Explanations_in_Vision_and_CVPR_2022_paper.html | CVPR 2022 | null |
Bringing Old Films Back to Life | Ziyu Wan, Bo Zhang, Dongdong Chen, Jing Liao | We present a learning-based framework, recurrent transformer network (RTN), to restore heavily degraded old films. Instead of performing frame-wise restoration, our method is based on the hidden knowledge learned from adjacent frames that contain abundant information about the occlusion, which is beneficial to restore challenging artifacts of each frame while ensuring temporal coherency. Moreover, contrasting the representation of the current frame and the hidden knowledge makes it possible to infer the scratch position in an unsupervised manner, and such defect localization generalizes well to real-world degradations. To better resolve mixed degradation and compensate for the flow estimation error during frame alignment, we propose to leverage more expressive transformer blocks for spatial restoration. Experiments on both synthetic dataset and real-world old films demonstrate the significant superiority of the proposed RTN over existing solutions. In addition, the same framework can effectively propagate the color from keyframes to the whole video, ultimately yielding compelling restored films. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wan_Bringing_Old_Films_Back_to_Life_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wan_Bringing_Old_Films_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.17276 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wan_Bringing_Old_Films_Back_to_Life_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wan_Bringing_Old_Films_Back_to_Life_CVPR_2022_paper.html | CVPR 2022 | null |
Sound and Visual Representation Learning With Multiple Pretraining Tasks | Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool | Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In this light, this work aims to combine Multiple SSL tasks (Multi-SSL) that generalizes well for all downstream tasks. For this study, we investigate binaural sounds and image data. For binaural sounds, we propose three SSL tasks namely, spatial alignment, temporal synchronization of foreground objects and binaural audio and temporal gap prediction. We investigate several approaches of Multi-SSL and give insights into the downstream task performance on video retrieval, spatial sound super resolution, and semantic prediction on the OmniAudio dataset. Our experiments on binaural sound representations demonstrate that Multi-SSL via incremental learning (IL) of SSL tasks outperforms single SSL task models and fully supervised models in the downstream task performance. As a check of applicability on other modality, we also formulate our Multi-SSL models for image representation learning and we use the recently proposed SSL tasks, MoCov2 and DenseCL. Here, Multi-SSL surpasses recent methods such as MoCov2, DenseCL and DetCo by 2.06%, 3.27% and 1.19% on VOC07 classification and +2.83, +1.56 and +1.61 AP on COCO detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Vasudevan_Sound_and_Visual_Representation_Learning_With_Multiple_Pretraining_Tasks_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.01046 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vasudevan_Sound_and_Visual_Representation_Learning_With_Multiple_Pretraining_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vasudevan_Sound_and_Visual_Representation_Learning_With_Multiple_Pretraining_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation | Yingzhi Tang, Yue Qian, Qijian Zhang, Yiming Zeng, Junhui Hou, Xuefei Zhe | We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, WarpingGAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also ingeniously utilize the principle of the discriminator and tailor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel generating mechanism, WarpingGAN, a single lightweight network after onetime training, is capable of efficiently generating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods to a large extent in terms of quantitative metrics, visual quality, and efficiency. The source code is publicly available at https://github.com/yztang4/WarpingGAN.git. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_WarpingGAN_Warping_Multiple_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12917 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_WarpingGAN_Warping_Multiple_Uniform_Priors_for_Adversarial_3D_Point_Cloud_CVPR_2022_paper.html | CVPR 2022 | null |
RePaint: Inpainting Using Denoising Diffusion Probabilistic Models | Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool | Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks. RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions. Github Repository: git.io/RePaint | https://openaccess.thecvf.com/content/CVPR2022/papers/Lugmayr_RePaint_Inpainting_Using_Denoising_Diffusion_Probabilistic_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lugmayr_RePaint_Inpainting_Using_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.09865 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lugmayr_RePaint_Inpainting_Using_Denoising_Diffusion_Probabilistic_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lugmayr_RePaint_Inpainting_Using_Denoising_Diffusion_Probabilistic_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Revealing Occlusions With 4D Neural Fields | Basile Van Hoorick, Purva Tendulkar, Dídac Surís, Dennis Park, Simon Stent, Carl Vondrick | For computer vision systems to operate in dynamic situations, they need to be able to represent and reason about object permanence. We introduce a framework for learning to estimate 4D visual representations from monocular RGB-D video, which is able to persist objects, even once they become obstructed by occlusions. Unlike traditional video representations, we encode point clouds into a continuous representation, which permits the model to attend across the spatiotemporal context to resolve occlusions. On two large video datasets that we release along with this paper, our experiments show that the representation is able to successfully reveal occlusions for several tasks, without any architectural changes. Visualizations show that the attention mechanism automatically learns to follow occluded objects. Since our approach can be trained end-to-end and is easily adaptable, we believe it will be useful for handling occlusions in many video understanding tasks. Data, code, and models are available at occlusions.cs.columbia.edu. | https://openaccess.thecvf.com/content/CVPR2022/papers/Van_Hoorick_Revealing_Occlusions_With_4D_Neural_Fields_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Van_Hoorick_Revealing_Occlusions_With_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Van_Hoorick_Revealing_Occlusions_With_4D_Neural_Fields_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Van_Hoorick_Revealing_Occlusions_With_4D_Neural_Fields_CVPR_2022_paper.html | CVPR 2022 | null |
Meta Agent Teaming Active Learning for Pose Estimation | Jia Gong, Zhipeng Fan, Qiuhong Ke, Hossein Rahmani, Jun Liu | The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire. To reduce the human efforts on pose annotations, we propose a novel Meta Agent Teaming Active Learning (MATAL) framework to actively select and label informative images for effective learning. Our MATAL formulates the image selection procedure as a Markov Decision Process and learns an optimal sampling policy that directly maximizes the performance of the pose estimator. Our framework consists of a novel state-action representation as well as a multi-agent team to enable batch sampling in the active learning procedure. The framework could be effectively optimized via Meta-Optimization to accelerate the adaptation to the gradually expanded labeled data during deployment. Finally, we show experimental results on both human hand and body pose estimation benchmark datasets and demonstrate that our method significantly outperforms all baselines continuously under the same amount of annotation budget. Moreover, to obtain similar pose estimation accuracy, our MATAL framework can save around 40% labeling efforts on average compared to state-of-the-art active learning frameworks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Gong_Meta_Agent_Teaming_Active_Learning_for_Pose_Estimation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Gong_Meta_Agent_Teaming_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_Meta_Agent_Teaming_Active_Learning_for_Pose_Estimation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Gong_Meta_Agent_Teaming_Active_Learning_for_Pose_Estimation_CVPR_2022_paper.html | CVPR 2022 | null |
Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild | Mingzhen Huang, Supreeth Narasimhaswamy, Saif Vazir, Haibin Ling, Minh Hoai | We propose HandLer, a novel convolutional architecture that can jointly detect and track hands online in unconstrained videos. HandLer is based on Cascade-RCNNwith additional three novel stages. The first stage is Forward Propagation, where the features from frame t-1 are propagated to frame t based on previously detected hands and their estimated motion. The second stage is the Detection and Backward Regression, which uses outputs from the forward propagation to detect hands for frame t and their relative offset in frame t-1. The third stage uses an off-the-shelf human pose method to link any fragmented hand tracklets. We train the forward propagation and backward regression and detection stages end-to-end together with the other Cascade-RCNN components.To train and evaluate HandLer, we also contribute YouTube-Hand, the first challenging large-scale dataset of unconstrained videos annotated with hand locations and their trajectories. Experiments on this dataset and other benchmarks show that HandLer outperforms the existing state-of-the-art tracking algorithms by a large margin. | https://openaccess.thecvf.com/content/CVPR2022/papers/Huang_Forward_Propagation_Backward_Regression_and_Pose_Association_for_Hand_Tracking_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Forward_Propagation_Backward_Regression_and_Pose_Association_for_Hand_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Forward_Propagation_Backward_Regression_and_Pose_Association_for_Hand_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
Pseudo-Q: Generating Pseudo Language Queries for Visual Grounding | Haojun Jiang, Yuanze Lin, Dongchen Han, Shiji Song, Gao Huang | Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches for this task are based on deep learning, which generally require expensive manually labeled image-query or patch-query pairs. To eliminate the heavy dependence on human annotations, we present a novel method, named Pseudo-Q, to automatically generate pseudo language queries for supervised training. Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module. Then, we design a task-related query prompt module to specifically tailor generated pseudo language queries for visual grounding tasks. Further, in order to fully capture the contextual relationships between images and language queries, we develop a visual-language model equipped with multi-level cross-modality attention mechanism. Extensive experimental results demonstrate that our method has two notable benefits: (1) it can reduce human annotation costs significantly, e.g., 31% on RefCOCO without degrading original model's performance under the fully supervised setting, and (2) without bells and whistles, it achieves superior or comparable performance compared to state-of-the-art weakly-supervised visual grounding methods on all the five datasets we have experimented. Code is available at https://github.com/LeapLabTHU/Pseudo-Q. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jiang_Pseudo-Q_Generating_Pseudo_Language_Queries_for_Visual_Grounding_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jiang_Pseudo-Q_Generating_Pseudo_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Pseudo-Q_Generating_Pseudo_Language_Queries_for_Visual_Grounding_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jiang_Pseudo-Q_Generating_Pseudo_Language_Queries_for_Visual_Grounding_CVPR_2022_paper.html | CVPR 2022 | null |
E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action Recognition | Chiara Plizzari, Mirco Planamente, Gabriele Goletto, Marco Cannici, Emanuele Gusso, Matteo Matteucci, Barbara Caputo | Event cameras are novel bio-inspired sensors, which asynchronously capture pixel-level intensity changes in the form of "events". Due to their sensing mechanism, event cameras have little to no motion blur, a very high temporal resolution and require significantly less power and memory than traditional frame-based cameras. These characteristics make them a perfect fit to several real-world applications such as egocentric action recognition on wearable devices, where fast camera motion and limited power challenge traditional vision sensors. However, the ever-growing field of event-based vision has, to date, overlooked the potential of event cameras in such applications. In this paper, we show that event data is a very valuable modality for egocentric action recognition. To do so, we introduce N-EPIC-Kitchens, the first event-based camera extension of the large-scale EPIC-Kitchens dataset. In this context, we propose two strategies: (i) directly processing event-camera data with traditional video-processing architectures (E^2(GO)) and (ii) using event-data to distill optical flow information E^2(GO)MO). On our proposed benchmark, we show that event data provides a comparable performance to RGB and optical flow, yet without any additional flow computation at deploy time, and an improved performance of up to 4% with respect to RGB only information. The N-EPIC-Kitchens dataset is available at https://github.com/EgocentricVision/N-EPIC-Kitchens. | https://openaccess.thecvf.com/content/CVPR2022/papers/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Plizzari_E2GOMOTION_Motion_Augmented_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Plizzari_E2GOMOTION_Motion_Augmented_Event_Stream_for_Egocentric_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework | Ningkai Mo, Wanshui Gan, Naoto Yokoya, Shifeng Chen | In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects. This framework is designed in a simple architecture that efficiently extracts point-wise features from RGB-D data using a fully convolutional network, called XYZNet, and directly regresses the 6D pose without any post refinement. In the case of symmetric object, one object has multiple ground-truth poses, and this one-to-many relationship may lead to estimation ambiguity. In order to solve this ambiguity problem, we design a symmetry-invariant pose distance metric, called average (maximum) grouped primitives distance or A(M)GPD. The proposed A(M)GPD loss can make the regression network converge to the correct state, i.e., all minima in the A(M)GPD loss surface are mapped to the correct poses. Extensive experiments on YCB-Video and T-LESS datasets demonstrate the proposed framework's substantially superior performance in top accuracy and low computational cost. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mo_ES6D_A_Computation_Efficient_and_Symmetry-Aware_6D_Pose_Regression_Framework_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mo_ES6D_A_Computation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.01080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mo_ES6D_A_Computation_Efficient_and_Symmetry-Aware_6D_Pose_Regression_Framework_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mo_ES6D_A_Computation_Efficient_and_Symmetry-Aware_6D_Pose_Regression_Framework_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Deep Image Restoration via Adaptive Stochastic Gradient Langevin Dynamics | Weixi Wang, Ji Li, Hui Ji | While supervised deep learning has been a prominent tool for solving many image restoration problems, there is an increasing interest on studying self-supervised or un- supervised methods to address the challenges and costs of collecting truth images. Based on the neuralization of a Bayesian estimator of the problem, this paper presents a self-supervised deep learning approach to general image restoration problems. The key ingredient of the neuralized estimator is an adaptive stochastic gradient Langevin dy- namics algorithm for efficiently sampling the posterior distri- bution of network weights. The proposed method is applied on two image restoration problems: compressed sensing and phase retrieval. The experiments on these applications showed that the proposed method not only outperformed existing non-learning and unsupervised solutions in terms of image restoration quality, but also is more computationally efficient. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Self-Supervised_Deep_Image_Restoration_via_Adaptive_Stochastic_Gradient_Langevin_Dynamics_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Self-Supervised_Deep_Image_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Deep_Image_Restoration_via_Adaptive_Stochastic_Gradient_Langevin_Dynamics_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Self-Supervised_Deep_Image_Restoration_via_Adaptive_Stochastic_Gradient_Langevin_Dynamics_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning | Hui Tang, Kui Jia | Semi-supervised learning (SSL) has been studied for a long time to solve vision tasks in data-efficient application scenarios. SSL aims to learn a good classification model using a few labeled data together with large-scale unlabeled data. Recent advances achieve the goal by combining multiple SSL techniques, e.g., self-training and consistency regularization. From unlabeled samples, they usually adopt a confidence filter (CF) to select reliable ones with high prediction confidence. In this work, we study whether the moderately confident samples are useless and how to select the useful ones to improve model optimization. To answer these problems, we propose a novel Taylor expansion inspired filtration (TEIF) framework, which admits the samples of moderate confidence with similar feature or gradient to the respective one averaged over the labeled and highly confident unlabeled data. It can produce a stable and new information induced network update, leading to better generalization. Two novel filters are derived from this framework and can be naturally explained in two perspectives. One is gradient synchronization filter (GSF), which strengthens the optimization dynamic of fully-supervised learning; it selects the samples whose gradients are similar to class-wise majority gradients. The other is prototype proximity filter (PPF), which involves more prototypical samples in training to learn better semantic representations; it selects the samples near class-wise prototypes. They can be integrated into SSL methods with CF. We use the state-of-the-art FixMatch as the baseline. Experiments on popular SSL benchmarks show that we achieve the new state of the art. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_Towards_Discovering_the_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_Towards_Discovering_the_Effectiveness_of_Moderately_Confident_Samples_for_Semi-Supervised_CVPR_2022_paper.html | CVPR 2022 | null |
OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization | Nanyang Ye, Kaican Li, Haoyue Bai, Runpeng Yu, Lanqing Hong, Fengwei Zhou, Zhenguo Li, Jun Zhu | Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically when encountering out-of-distribution (OoD) data, i.e., when training and test data are sampled from different distributions. While a plethora of algorithms have been proposed for OoD generalization, our understanding of the data used to train and evaluate these algorithms remains stagnant. In this work, we first identify and measure two distinct kinds of distribution shifts that are ubiquitous in various datasets. Next, through extensive experiments, we compare OoD generalization algorithms across two groups of benchmarks, each dominated by one of the distribution shifts, revealing their strengths on one shift as well as limitations on the other shift. Overall, we position existing datasets and algorithms from different research areas seemingly unconnected into the same coherent picture. It may serve as a foothold that can be resorted to by future OoD generalization research. Our code is available at https://github.com/ynysjtu/ood_bench. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ye_OoD-Bench_Quantifying_and_Understanding_Two_Dimensions_of_Out-of-Distribution_Generalization_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ye_OoD-Bench_Quantifying_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_OoD-Bench_Quantifying_and_Understanding_Two_Dimensions_of_Out-of-Distribution_Generalization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ye_OoD-Bench_Quantifying_and_Understanding_Two_Dimensions_of_Out-of-Distribution_Generalization_CVPR_2022_paper.html | CVPR 2022 | null |
An Empirical Study of Training End-to-End Vision-and-Language Transformers | Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, Michael Zeng | Vision-and-language (VL) pre-training has proven to be highly effective on various VL downstream tasks. While recent work has shown that fully transformer-based VL models can be more efficient than previous region-feature-based methods, their performance on downstream tasks often degrades significantly. In this paper, we present METER, a Multimodal End-to-end TransformER framework, through which we investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner. Specifically, we dissect the model designs along multiple dimensions: vision encoders (e.g., CLIP-ViT, Swin transformer), text encoders (e.g., RoBERTa, DeBERTa), multimodal fusion module (e.g., merged attention vs. co-attention), architectural design (e.g., encoder-only vs. encoder-decoder), and pre-training objectives (e.g., masked image modeling). We conduct comprehensive experiments and provide insights on how to train a performant VL transformer while maintaining fast inference speed. Notably, our best model achieves an accuracy of 77.64% on the VQAv2 test-std set using only 4M images for pre-training, surpassing the state-of-the-art region-feature-based model by 1.04%, and outperforming the previous best fully transformer-based model by 1.6%. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dou_An_Empirical_Study_of_Training_End-to-End_Vision-and-Language_Transformers_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dou_An_Empirical_Study_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.02387 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dou_An_Empirical_Study_of_Training_End-to-End_Vision-and-Language_Transformers_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dou_An_Empirical_Study_of_Training_End-to-End_Vision-and-Language_Transformers_CVPR_2022_paper.html | CVPR 2022 | null |
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification | Zongbo Han, Fan Yang, Junzhou Huang, Changqing Zhang, Jianhua Yao | Integration of heterogeneous and high-dimensional data (e.g., multiomics) is becoming increasingly important. Existing multimodal classification algorithms mainly focus on improving performance by exploiting the complementarity from different modalities. However, conventional approaches are basically weak in providing trustworthy multimodal fusion, especially for safety-critical applications (e.g., medical diagnosis). For this issue, we propose a novel trustworthy multimodal classification algorithm termed Multimodal Dynamics, which dynamically evaluates both the feature-level and modality-level informativeness for different samples and thus trustworthily integrates multiple modalities. Specifically, a sparse gating is introduced to capture the information variation of each within-modality feature and the true class probability is employed to assess the classification confidence of each modality. Then a transparent fusion algorithm based on the dynamical informativeness estimation strategy is induced. To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification. Extensive experiments are conducted on multimodal medical classification datasets. In these experiments, superior performance and trustworthiness of our algorithm are clearly validated compared to the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Han_Multimodal_Dynamics_Dynamical_Fusion_for_Trustworthy_Multimodal_Classification_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Han_Multimodal_Dynamics_Dynamical_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Multimodal_Dynamics_Dynamical_Fusion_for_Trustworthy_Multimodal_Classification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Han_Multimodal_Dynamics_Dynamical_Fusion_for_Trustworthy_Multimodal_Classification_CVPR_2022_paper.html | CVPR 2022 | null |
The Neurally-Guided Shape Parser: Grammar-Based Labeling of 3D Shape Regions With Approximate Inference | R. Kenny Jones, Aalia Habib, Rana Hanocka, Daniel Ritchie | We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape. NGSP solves this problem via MAP inference, modeling the posterior probability of a label assignment conditioned on an input shape with a learned likelihood function. To make this search tractable, NGSP employs a neural guide network that learns to approximate the posterior. NGSP finds high-probability label assignments by first sampling proposals with the guide network and then evaluating each proposal under the full likelihood. We evaluate NGSP on the task of fine-grained semantic segmentation of manufactured 3D shapes from PartNet, where shapes have been decomposed into regions that correspond to part instance over-segmentations. We find that NGSP delivers significant performance improvements over comparison methods that (i) use regions to group per-point predictions, (ii) use regions as a self-supervisory signal or (iii) assign labels to regions under alternative formulations. Further, we show that NGSP maintains strong performance even with limited labeled data or noisy input shape regions. Finally, we demonstrate that NGSP can be directly applied to CAD shapes found in online repositories and validate its effectiveness with a perceptual study. | https://openaccess.thecvf.com/content/CVPR2022/papers/Jones_The_Neurally-Guided_Shape_Parser_Grammar-Based_Labeling_of_3D_Shape_Regions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Jones_The_Neurally-Guided_Shape_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2106.12026 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Jones_The_Neurally-Guided_Shape_Parser_Grammar-Based_Labeling_of_3D_Shape_Regions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Jones_The_Neurally-Guided_Shape_Parser_Grammar-Based_Labeling_of_3D_Shape_Regions_CVPR_2022_paper.html | CVPR 2022 | null |
Unsupervised Homography Estimation With Coplanarity-Aware GAN | Mingbo Hong, Yuhang Lu, Nianjin Ye, Chunyu Lin, Qijun Zhao, Shuaicheng Liu | Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane induced parallax, which will make the predicted homography compromised on multiple planes. In this work, we propose a novel method HomoGAN to guide unsupervised homography estimation to focus on the dominant plane. First, a multi-scale transformer network is designed to predict homography from the feature pyramids of input images in a coarse-to-fine fashion. Moreover, we propose an unsupervised GAN to impose coplanarity constraint on the predicted homography, which is realized by using a generator to predict a mask of aligned regions, and then a discriminator to check if two masked feature maps can be induced by a single homography. To validate the effectiveness of HomoGAN and its components, we conduct extensive experiments on a large-scale dataset, and results show that our matching error is 22% lower than the previous SOTA method. Our code will be publicly available. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hong_Unsupervised_Homography_Estimation_With_Coplanarity-Aware_GAN_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Hong_Unsupervised_Homography_Estimation_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.03821 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Unsupervised_Homography_Estimation_With_Coplanarity-Aware_GAN_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hong_Unsupervised_Homography_Estimation_With_Coplanarity-Aware_GAN_CVPR_2022_paper.html | CVPR 2022 | null |
LIFT: Learning 4D LiDAR Image Fusion Transformer for 3D Object Detection | Yihan Zeng, Da Zhang, Chunwei Wang, Zhenwei Miao, Ting Liu, Xin Zhan, Dayang Hao, Chao Ma | LiDAR and camera are two common sensors to collect data in time for 3D object detection under the autonomous driving context. Though the complementary information across sensors and time has great potential of benefiting 3D perception, taking full advantage of sequential cross-sensor data still remains challenging. In this paper, we propose a novel LiDAR Image Fusion Transformer (LIFT) to model the mutual interaction relationship of cross-sensor data over time. LIFT learns to align the input 4D sequential cross-sensor data to achieve multi-frame multi-modal information aggregation. To alleviate computational load, we project both point clouds and images into the bird-eye-view maps to compute sparse grid-wise self-attention. LIFT also benefits from a cross-sensor and cross-time data augmentation scheme. We evaluate the proposed approach on the challenging nuScenes and Waymo datasets, where our LIFT performs well over the state-of-the-art and strong baselines. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zeng_LIFT_Learning_4D_LiDAR_Image_Fusion_Transformer_for_3D_Object_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zeng_LIFT_Learning_4D_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_LIFT_Learning_4D_LiDAR_Image_Fusion_Transformer_for_3D_Object_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_LIFT_Learning_4D_LiDAR_Image_Fusion_Transformer_for_3D_Object_CVPR_2022_paper.html | CVPR 2022 | null |
AutoLoss-Zero: Searching Loss Functions From Scratch for Generic Tasks | Hao Li, Tianwen Fu, Jifeng Dai, Hongsheng Li, Gao Huang, Xizhou Zhu | Significant progress has been achieved in automating the design of various components in deep networks. However, the automatic design of loss functions for generic tasks with various evaluation metrics remains under-investigated. Previous works on handcrafting loss functions heavily rely on human expertise, which limits their extensibility. Meanwhile, searching for loss functions is nontrivial due to the vast search space. Existing efforts mainly tackle the issue by employing task-specific heuristics on specific tasks and particular metrics. Such work cannot be extended to other tasks without arduous human effort. In this paper, we propose AutoLoss-Zero, which is a general framework for searching loss functions from scratch for generic tasks. Specifically, we design an elementary search space composed only of primitive mathematical operators to accommodate the heterogeneous tasks and evaluation metrics. A variant of the evolutionary algorithm is employed to discover loss functions in the elementary search space. A loss-rejection protocol and a gradient-equivalence-check strategy are developed so as to improve the search efficiency, which are applicable to generic tasks. Extensive experiments on various computer vision tasks demonstrate that our searched loss functions are on par with or superior to existing loss functions, which generalize well to different datasets and networks. Code shall be released. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_AutoLoss-Zero_Searching_Loss_Functions_From_Scratch_for_Generic_Tasks_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_AutoLoss-Zero_Searching_Loss_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_AutoLoss-Zero_Searching_Loss_Functions_From_Scratch_for_Generic_Tasks_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_AutoLoss-Zero_Searching_Loss_Functions_From_Scratch_for_Generic_Tasks_CVPR_2022_paper.html | CVPR 2022 | null |
PatchNet: A Simple Face Anti-Spoofing Framework via Fine-Grained Patch Recognition | Chien-Yi Wang, Yu-Ding Lu, Shang-Ta Yang, Shang-Hong Lai | Face anti-spoofing (FAS) plays a critical role in securing face recognition systems from different presentation attacks. Previous works leverage auxiliary pixel-level supervision and domain generalization approaches to address unseen spoof types. However, the local characteristics of image captures, i.e., capturing devices and presenting materials, are ignored in existing works and we argue that such information is required for networks to discriminate between live and spoof images. In this work, we propose PatchNet which reformulates face anti-spoofing as a fine-grained patch-type recognition problem. To be specific, our framework recognizes the combination of capturing devices and presenting materials based on the patches cropped from non-distorted face images. This reformulation can largely improve the data variation and enforce the network to learn discriminative feature from local capture patterns. In addition, to further improve the generalization ability of the spoof feature, we propose the novel Asymmetric Margin-based Classification Loss and Self-supervised Similarity Loss to regularize the patch embedding space. Our experimental results verify our assumption and show that the model is capable of recognizing unseen spoof types robustly by only looking at local regions. Moreover, the fine-grained and patch-level reformulation of FAS outperforms the existing approaches on intra-dataset, cross-dataset, and domain generalization benchmarks. Furthermore, our PatchNet framework can enable practical applications like Few-Shot Reference-based FAS and facilitate future exploration of spoof-related intrinsic cues. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_PatchNet_A_Simple_Face_Anti-Spoofing_Framework_via_Fine-Grained_Patch_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_PatchNet_A_Simple_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14325 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PatchNet_A_Simple_Face_Anti-Spoofing_Framework_via_Fine-Grained_Patch_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_PatchNet_A_Simple_Face_Anti-Spoofing_Framework_via_Fine-Grained_Patch_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
OnePose: One-Shot Object Pose Estimation Without CAD Models | Jiaming Sun, Zihao Wang, Siyu Zhang, Xingyi He, Hongcheng Zhao, Guofeng Zhang, Xiaowei Zhou | We propose a new method named OnePose for object pose estimation. Unlike existing instance-level or category-level methods, OnePose does not rely on CAD models and can handle objects in arbitrary categories without instance- or category-specific network training. OnePose draws the idea from visual localization and only requires a simple RGB video scan of the object to build a sparse SfM model of the object. Then, this model is registered to new query images with a generic feature matching network. To mitigate the slow runtime of existing visual localization methods, we propose a new graph attention network that directly matches 2D interest points in the query image with the 3D points in the SfM model, resulting in efficient and robust pose estimation. Combined with a feature-based pose tracker, OnePose is able to stably detect and track 6D poses of everyday household objects in real-time. We also collected a large-scale dataset that consists of 450 se- quences of 150 objects. Code and data are available at the project page: https://zju3dv.github.io/onepose/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_OnePose_One-Shot_Object_Pose_Estimation_Without_CAD_Models_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2205.12257 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_OnePose_One-Shot_Object_Pose_Estimation_Without_CAD_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_OnePose_One-Shot_Object_Pose_Estimation_Without_CAD_Models_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos | Reza Ghoddoosian, Isht Dwivedi, Nakul Agarwal, Chiho Choi, Behzad Dariush | This paper addresses a new problem of weakly-supervised online action segmentation in instructional videos. We present a framework to segment streaming videos online at test time using Dynamic Programming and show its advantages over greedy sliding window approach. We improve our framework by introducing the Online-Offline Discrepancy Loss (OODL) to encourage the segmentation results to have a higher temporal consistency. Furthermore, only during training, we exploit frame-wise correspondence between multiple views as supervision for training weakly-labeled instructional videos. In particular, we investigate three different multi-view inference techniques to generate more accurate frame-wise pseudo ground-truth with no additional annotation cost. We present results and ablation studies on two benchmark multi-view datasets, Breakfast and IKEA ASM. Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ghoddoosian_Weakly-Supervised_Online_Action_Segmentation_in_Multi-View_Instructional_Videos_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ghoddoosian_Weakly-Supervised_Online_Action_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.13309 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ghoddoosian_Weakly-Supervised_Online_Action_Segmentation_in_Multi-View_Instructional_Videos_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ghoddoosian_Weakly-Supervised_Online_Action_Segmentation_in_Multi-View_Instructional_Videos_CVPR_2022_paper.html | CVPR 2022 | null |
Rethinking Minimal Sufficient Representation in Contrastive Learning | Haoqing Wang, Xun Guo, Zhi-Hong Deng, Yan Lu | Contrastive learning between different views of the data achieves outstanding success in the field of self-supervised representation learning and the learned representations are useful in broad downstream tasks. Since all supervision information for one view comes from the other view, contrastive learning approximately obtains the minimal sufficient representation which contains the shared information and eliminates the non-shared information between views. Considering the diversity of the downstream tasks, it cannot be guaranteed that all task-relevant information is shared between views. Therefore, we assume the non-shared task-relevant information cannot be ignored and theoretically prove that the minimal sufficient representation in contrastive learning is not sufficient for the downstream tasks, which causes performance degradation. This reveals a new problem that the contrastive learning models have the risk of over-fitting to the shared information between views. To alleviate this problem, we propose to increase the mutual information between the representation and input as regularization to approximately introduce more task-relevant information, since we cannot utilize any downstream task information during training. Extensive experiments verify the rationality of our analysis and the effectiveness of our method. It significantly improves the performance of several classic contrastive learning models in downstream tasks. Our code is available at https://github.com/Haoqing-Wang/InfoCL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Rethinking_Minimal_Sufficient_Representation_in_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Rethinking_Minimal_Sufficient_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.07004 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Rethinking_Minimal_Sufficient_Representation_in_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Rethinking_Minimal_Sufficient_Representation_in_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Disentangling Visual Embeddings for Attributes and Objects | Nirat Saini, Khoi Pham, Abhinav Shrivastava | We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct features associated with attributes. To overcome this challenge, these studies employ supervision from the linguistic space, and use pre-trained word embeddings to better separate and compose attribute-object pairs for recognition. Analogous to linguistic embedding space, which already has unique and agnostic embeddings for object and attribute, we shift the focus back to the visual space and propose a novel architecture that can disentangle attribute and object features in the visual space. We use visual decomposed features to hallucinate embeddings that are representative for the seen and novel compositions to better regularize the learning of our model. Extensive experiments show that our method outperforms existing work with significant margin on three datasets: MIT-States, UT-Zappos, and a new benchmark created based on VAW. | https://openaccess.thecvf.com/content/CVPR2022/papers/Saini_Disentangling_Visual_Embeddings_for_Attributes_and_Objects_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Saini_Disentangling_Visual_Embeddings_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2205.08536 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Saini_Disentangling_Visual_Embeddings_for_Attributes_and_Objects_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Saini_Disentangling_Visual_Embeddings_for_Attributes_and_Objects_CVPR_2022_paper.html | CVPR 2022 | null |
Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels | Yikai Wang, Xinwei Sun, Yanwei Fu | Noisy training set usually leads to the degradation of generalization and robustness of neural networks. In this paper, we propose using a theoretically guaranteed noisy label detection framework to detect and remove noisy data for Learning with Noisy Labels (LNL). Specifically, we design a penalized regression to model the linear relation between network features and one-hot labels, where the noisy data are identified by the non-zero mean shift parameters solved in the regression model. To make the framework scalable to datasets that contain a large number of categories and training data, we propose a split algorithm to divide the whole training set into small pieces that can be solved by the penalized regression in parallel, leading to the Scalable Penalized Regression (SPR) framework. We provide the non-asymptotic probabilistic condition for SPR to correctly identify the noisy data. While SPR can be regarded as a sample selection module for standard supervised training pipeline, we further combine it with semi-supervised algorithm to further exploit the support of noisy data as unlabeled data. Experimental results on several benchmark datasets and real-world noisy datasets show the effectiveness of our framework. Our code and pretrained models are released at https://github.com/Yikai-Wang/SPR-LNL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Scalable_Penalized_Regression_for_Noise_Detection_in_Learning_With_Noisy_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.07788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Scalable_Penalized_Regression_for_Noise_Detection_in_Learning_With_Noisy_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Scalable_Penalized_Regression_for_Noise_Detection_in_Learning_With_Noisy_CVPR_2022_paper.html | CVPR 2022 | null |
Effective Conditioned and Composed Image Retrieval Combining CLIP-Based Features | Alberto Baldrati, Marco Bertini, Tiberio Uricchio, Alberto Del Bimbo | Conditioned and composed image retrieval extend CBIR systems by combining a query image with an additional text that expresses the intent of the user, describing additional requests w.r.t. the visual content of the query image. This type of search is interesting for e-commerce applications, e.g. to develop interactive multimodal searches and chatbots. In this demo, we present an interactive system based on a combiner network, trained using contrastive learning, that combines visual and textual features obtained from the OpenAI CLIP network to address conditioned CBIR. The system can be used to improve e-shop search engines. For example, considering the fashion domain it lets users search for dresses, shirts and toptees using a candidate start image and expressing some visual differences w.r.t. its visual content, e.g. asking to change color, pattern or shape. The proposed network obtains state-of-the-art performance on the FashionIQ dataset and on the more recent CIRR dataset, showing its applicability to the fashion domain for conditioned retrieval, and to more generic content considering the more general task of composed image retrieval. | https://openaccess.thecvf.com/content/CVPR2022/papers/Baldrati_Effective_Conditioned_and_Composed_Image_Retrieval_Combining_CLIP-Based_Features_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Baldrati_Effective_Conditioned_and_Composed_Image_Retrieval_Combining_CLIP-Based_Features_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Baldrati_Effective_Conditioned_and_Composed_Image_Retrieval_Combining_CLIP-Based_Features_CVPR_2022_paper.html | CVPR 2022 | null |
Registering Explicit to Implicit: Towards High-Fidelity Garment Mesh Reconstruction From Single Images | Heming Zhu, Lingteng Qiu, Yuda Qiu, Xiaoguang Han | Fueled by the power of deep learning techniques and implicit shape learning, recent advances in single-image human digitalization have reached unprecedented accuracy and could recover fine-grained surface details such as garment wrinkles. However, a common problem for the implicit-based methods is that they cannot produce separated and topology-consistent mesh for each garment piece, which is crucial for the current 3D content creation pipeline. To address this issue, we proposed a novel geometry inference framework ReEF that reconstructs topology- consistent layered garment mesh by registering the explicit garment template to the whole-body implicit fields predicted from single images. Experiments demonstrate that our method notably outperforms the counterparts on single-image layered garment reconstruction and could bring high-quality digital assets for further content creation. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhu_Registering_Explicit_to_Implicit_Towards_High-Fidelity_Garment_Mesh_Reconstruction_From_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhu_Registering_Explicit_to_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.15007 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Registering_Explicit_to_Implicit_Towards_High-Fidelity_Garment_Mesh_Reconstruction_From_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhu_Registering_Explicit_to_Implicit_Towards_High-Fidelity_Garment_Mesh_Reconstruction_From_CVPR_2022_paper.html | CVPR 2022 | null |
Federated Class-Incremental Learning | Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, Qi Zhu | Federated learning (FL) has attracted growing attentions via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume object classes of the overall framework are fixed over time. It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes. Moreover, new clients with unseen new classes may participate in the FL training, further aggravating the catastrophic forgetting of global model. To address these challenges, we develop a novel Global-Local Forgetting Compensation (GLFC) model, to learn a global class-incremental model for alleviating the catastrophic forgetting from both local and global perspectives. Specifically, to address local forgetting caused by class imbalance at the local clients, we design a class-aware gradient compensation loss and a class-semantic relation distillation loss to balance the forgetting of old classes and distill consistent inter-class relations across tasks. To tackle the global forgetting brought by the non-i.i.d class imbalance across clients, we propose a proxy server that selects the best old global model to assist the local relation distillation. Moreover, a prototype gradient-based communication mechanism is developed to protect the privacy. Our model outperforms state-of-the-art methods by 4.4% 15.1% in terms of average accuracy on representative benchmark datasets. The code is available at https://github.com/conditionWang/FCIL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_Federated_Class-Incremental_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_Federated_Class-Incremental_Learning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11473 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Federated_Class-Incremental_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Federated_Class-Incremental_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
MiniViT: Compressing Vision Transformers With Weight Multiplexing | Jinnian Zhang, Houwen Peng, Kan Wu, Mengchen Liu, Bin Xiao, Jianlong Fu, Lu Yuan | Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT models suffer from huge number of parameters, restricting their applicability on devices with limited computation. To alleviate this problem, we propose MiniViT, a new compression framework, which achieves parameter reduction in vision transformers while retaining the same performance. The central idea of MiniViT is to multiplex the weights of consecutive transformer blocks. More specifically, we make the weights shared across layers, while imposing a transformation on the weights to increase diversity. Weight distillation over self-attention is also applied to transfer knowledge from large-scale ViT models to weight-multiplexed compact models. Comprehensive experiments demonstrate the efficacy of MiniViT, showing that it can reduce the size of the pre-trained Swin-B transformer by 48%, while achieving an increase of 1.0% in top-1 accuracy on ImageNet. Moreover, using a single-layer parameters, MiniViT is able to compress DeiT-B by 9.7 times from 86M to 9M parameters, without seriously compromising the performance. Finally, we verify the transferability of MiniViT by reporting its performance on downstream benchmarks. Code and models are available at here. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_MiniViT_Compressing_Vision_Transformers_With_Weight_Multiplexing_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_MiniViT_Compressing_Vision_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.07154 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_MiniViT_Compressing_Vision_Transformers_With_Weight_Multiplexing_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_MiniViT_Compressing_Vision_Transformers_With_Weight_Multiplexing_CVPR_2022_paper.html | CVPR 2022 | null |
Practical Stereo Matching via Cascaded Recurrent Network With Adaptive Correlation | Jiankun Li, Peisen Wang, Pengfei Xiong, Tao Cai, Ziwei Yan, Lei Yang, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu | With the advent of convolutional neural networks, stereo matching algorithms have recently gained tremendous progress. However, it remains a great challenge to accurately extract disparities from real-world image pairs taken by consumer-level devices like smartphones, due to practical complicating factors such as thin structures, non-ideal rectification, camera module inconsistencies and various hard-case scenes. In this paper, we propose a set of innovative designs to tackle the problem of practical stereo matching: 1) to better recover fine depth details, we design a hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference; 2) we propose an adaptive group correlation layer to mitigate the impact of erroneous rectification; 3) we introduce a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes. Our results not only rank 1st on both Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art methods by a notable margin, but also exhibit high-quality details for real-life photos, which clearly demonstrates the efficacy of our contributions. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Practical_Stereo_Matching_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11483 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Practical_Stereo_Matching_via_Cascaded_Recurrent_Network_With_Adaptive_Correlation_CVPR_2022_paper.html | CVPR 2022 | null |
D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions | Sammy Christen, Muhammed Kocabas, Emre Aksan, Jemin Hwangbo, Jie Song, Otmar Hilliges | We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose. This is challenging, because it requires reasoning about the complex articulation of the human hand and the intricate physical interaction with the object. We propose a novel method that frames this problem in the reinforcement learning framework and leverages a physics simulation, both to learn and to evaluate such dynamic interactions. A hierarchical approach decomposes the task into low-level grasping and high-level motion synthesis. It can be used to generate novel hand sequences that approach, grasp, and move an object to a desired location, while retaining human-likeness. We show that our approach leads to stable grasps and generates a wide range of motions. Furthermore, even imperfect labels can be corrected by our method to generate dynamic interaction sequences. | https://openaccess.thecvf.com/content/CVPR2022/papers/Christen_D-Grasp_Physically_Plausible_Dynamic_Grasp_Synthesis_for_Hand-Object_Interactions_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Christen_D-Grasp_Physically_Plausible_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Christen_D-Grasp_Physically_Plausible_Dynamic_Grasp_Synthesis_for_Hand-Object_Interactions_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Christen_D-Grasp_Physically_Plausible_Dynamic_Grasp_Synthesis_for_Hand-Object_Interactions_CVPR_2022_paper.html | CVPR 2022 | null |
Show, Deconfound and Tell: Image Captioning With Causal Inference | Bing Liu, Dong Wang, Xu Yang, Yong Zhou, Rui Yao, Zhiwen Shao, Jiaqi Zhao | The transformer-based encoder-decoder framework has shown remarkable performance in image captioning. However, most transformer-based captioning methods ever overlook two kinds of elusive confounders: the visual confounder and the linguistic confounder, which generally lead to harmful bias, induce the spurious correlations during training, and degrade the model generalization. In this paper, we first use Structural Causal Models (SCMs) to show how two confounders damage the image captioning. Then we apply the backdoor adjustment to propose a novel causal inference based image captioning (CIIC) framework, which consists of an interventional object detector (IOD) and an interventional transformer decoder (ITD) to jointly confront both confounders. In the encoding stage, the IOD is able to disentangle the region-based visual features by deconfounding the visual confounder. In the decoding stage, the ITD introduces causal intervention into the transformer decoder and deconfounds the visual and linguistic confounders simultaneously. Two modules collaborate with each other to alleviate the spurious correlations caused by the unobserved confounders. When tested on MSCOCO, our proposal significantly outperforms the state-of-the-art encoder-decoder models on Karpathy split and online test split. Code is published in https: //github.com/CUMTGG/CIIC. | https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_Show_Deconfound_and_Tell_Image_Captioning_With_Causal_Inference_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liu_Show_Deconfound_and_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Show_Deconfound_and_Tell_Image_Captioning_With_Causal_Inference_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Show_Deconfound_and_Tell_Image_Captioning_With_Causal_Inference_CVPR_2022_paper.html | CVPR 2022 | null |
Extracting Triangular 3D Models, Materials, and Lighting From Images | Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, Sanja Fidler | We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, which typically produce entangled 3D representations encoded in neural networks, we output triangle meshes with spatially-varying materials and environment lighting that can be deployed in any traditional graphics engine unmodified. We leverage recent work in differentiable rendering, coordinate-based networks to compactly represent volumetric texturing, alongside differentiable marching tetrahedrons to enable gradient-based optimization directly on the surface mesh. Finally, we introduce a differentiable formulation of the split sum approximation of environment lighting to efficiently recover all-frequency lighting. Experiments show our extracted models used in advanced scene editing, material decomposition, and high quality view interpolation, all running at interactive rates in triangle-based renderers (rasterizers and path tracers). | https://openaccess.thecvf.com/content/CVPR2022/papers/Munkberg_Extracting_Triangular_3D_Models_Materials_and_Lighting_From_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Munkberg_Extracting_Triangular_3D_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2111.12503 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Munkberg_Extracting_Triangular_3D_Models_Materials_and_Lighting_From_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Munkberg_Extracting_Triangular_3D_Models_Materials_and_Lighting_From_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph Propagation | Hanyu Shi, Jiacheng Wei, Ruibo Li, Fayao Liu, Guosheng Lin | Existing point cloud segmentation methods require a large amount of annotated data, especially for the outdoor point cloud scene. Due to the complexity of the outdoor 3D scenes, manual annotations on the outdoor point cloud scene are time-consuming and expensive. In this paper, we study how to achieve scene understanding with limited annotated data. Treating 100 consecutive frames as a sequence, we divide the whole dataset into a series of sequences and annotate only 0.1% points in the first frame of each sequence to reduce the annotation requirements. This leads to a total annotation budget of 0.001%. We propose a novel temporal-spatial framework for effective weakly supervised learning to generate high-quality pseudo labels from these limited annotated data. Specifically, the framework contains two modules: an matching module in temporal dimension to propagate pseudo labels across different frames, and a graph propagation module in spatial dimension to propagate the information of pseudo labels to the entire point clouds in each frame. With only 0.001% annotations for training, experimental results on both SemanticKITTI and SemanticPOSS shows our weakly supervised two-stage framework is comparable to some existing fully supervised methods. We also evaluate our framework with 0.005% initial annotations on SemanticKITTI, and achieve a result close to fully supervised backbone model. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shi_Weakly_Supervised_Segmentation_on_Outdoor_4D_Point_Clouds_With_Temporal_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shi_Weakly_Supervised_Segmentation_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Weakly_Supervised_Segmentation_on_Outdoor_4D_Point_Clouds_With_Temporal_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shi_Weakly_Supervised_Segmentation_on_Outdoor_4D_Point_Clouds_With_Temporal_CVPR_2022_paper.html | CVPR 2022 | null |
ImFace: A Nonlinear 3D Morphable Face Model With Implicit Neural Representations | Mingwu Zheng, Hongyu Yang, Di Huang, Liming Chen | Precise representations of 3D faces are beneficial to various computer vision and graphics applications. Due to the data discretization and model linearity however, it remains challenging to capture accurate identity and expression clues in current studies. This paper presents a novel 3D morphable face model, namely ImFace, to learn a nonlinear and continuous space with implicit neural representations. It builds two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, and designs an improved learning strategy to extend embeddings of expressions to allow more diverse changes. We further introduce a Neural Blend-Field to learn sophisticated details by adaptively blending a series of local fields. In addition to ImFace, an effective preprocessing pipeline is proposed to address the issue of watertight input requirement in implicit representations, enabling them to work with common facial surfaces for the first time. Extensive experiments are performed to demonstrate the superiority of ImFace. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_ImFace_A_Nonlinear_3D_Morphable_Face_Model_With_Implicit_Neural_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zheng_ImFace_A_Nonlinear_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.14510 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_ImFace_A_Nonlinear_3D_Morphable_Face_Model_With_Implicit_Neural_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_ImFace_A_Nonlinear_3D_Morphable_Face_Model_With_Implicit_Neural_CVPR_2022_paper.html | CVPR 2022 | null |
MobRecon: Mobile-Friendly Hand Mesh Reconstruction From Monocular Image | Xingyu Chen, Yufeng Liu, Yajiao Dong, Xiong Zhang, Chongyang Ma, Yanmin Xiong, Yuan Zhang, Xiaoyan Guo | In this work, we propose a framework for single-view hand mesh reconstruction, which can simultaneously achieve high reconstruction accuracy, fast inference speed, and temporal coherence. Specifically, for 2D encoding, we propose lightweight yet effective stacked structures. Regarding 3D decoding, we provide an efficient graph operator, namely depth-separable spiral convolution. Moreover, we present a novel feature lifting module for bridging the gap between 2D and 3D representations. This module begins with a map-based position regression (MapReg) block to integrate the merits of both heatmap encoding and position regression paradigms for improved 2D accuracy and temporal coherence. Furthermore, MapReg is followed by pose pooling and pose-to-vertex lifting approaches, which transform 2D pose encodings to semantic features of 3D vertices. Overall, our hand reconstruction framework, called MobRecon, comprises affordable computational costs and miniature model size, which reaches a high inference speed of 83FPS on Apple A14 CPU. Extensive experiments on popular datasets such as FreiHAND, RHD, and HO3Dv2 demonstrate that our MobRecon achieves superior performance on reconstruction accuracy and temporal coherence. Our code is publicly available at https://github.com/SeanChenxy/HandMesh. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_MobRecon_Mobile-Friendly_Hand_Mesh_Reconstruction_From_Monocular_Image_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_MobRecon_Mobile-Friendly_Hand_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02753 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MobRecon_Mobile-Friendly_Hand_Mesh_Reconstruction_From_Monocular_Image_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_MobRecon_Mobile-Friendly_Hand_Mesh_Reconstruction_From_Monocular_Image_CVPR_2022_paper.html | CVPR 2022 | null |
Layered Depth Refinement With Mask Guidance | Soo Ye Kim, Jianming Zhang, Simon Niklaus, Yifei Fan, Simon Chen, Zhe Lin, Munchurl Kim | Depth maps are used in a wide range of applications from 3D rendering to 2D image effects such as Bokeh. However, those predicted by single image depth estimation (SIDE) models often fail to capture isolated holes in objects and/or have inaccurate boundary regions. Meanwhile, high-quality masks are much easier to obtain, using commercial auto-masking tools or off-the-shelf methods of segmentation and matting or even by manual editing. Hence, in this paper, we formulate a novel problem of mask-guided depth refinement that utilizes a generic mask to refine the depth prediction of SIDE models. Our framework performs layered refinement and inpainting/outpainting, decomposing the depth map into two separate layers signified by the mask and the inverse mask. As datasets with both depth and mask annotations are scarce, we propose a self-supervised learning scheme that uses arbitrary masks and RGB-D datasets. We empirically show that our method is robust to different types of masks and initial depth predictions, accurately refining depth values in inner and outer mask boundary regions. We further analyze our model with an ablation study and demonstrate results on real applications. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kim_Layered_Depth_Refinement_CVPR_2022_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kim_Layered_Depth_Refinement_With_Mask_Guidance_CVPR_2022_paper.html | CVPR 2022 | null |
Parameter-Free Online Test-Time Adaptation | Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, Luca Bertinetto | Training state-of-the-art vision models has become prohibitively expensive for researchers and practitioners. For the sake of accessibility and resource reuse, it is important to focus on adapting these models to a variety of downstream scenarios. An interesting and practical paradigm is online test-time adaptation, according to which training data is inaccessible, no labelled data from the test distribution is available, and adaptation can only happen at test time and on a handful of samples. In this paper, we investigate how test-time adaptation methods fare for a number of pre-trained models on a variety of real-world scenarios, significantly extending the way they have been originally evaluated. We show that they perform well only in narrowly-defined experimental setups and sometimes fail catastrophically when their hyperparameters are not selected for the same scenario in which they are being tested. Motivated by the inherent uncertainty around the conditions that will ultimately be encountered at test time, we propose a particularly "conservative" approach, which addresses the problem with a Laplacian Adjusted Maximum-likelihood Estimation (LAME) objective. By adapting the model's output (not its parameters), and solving our objective with an efficient concave-convex procedure, our approach exhibits a much higher average accuracy across scenarios than existing methods, while being notably faster and have a much lower memory footprint. The code is available at https://github.com/fiveai/LAME. | https://openaccess.thecvf.com/content/CVPR2022/papers/Boudiaf_Parameter-Free_Online_Test-Time_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Boudiaf_Parameter-Free_Online_Test-Time_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.05718 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Boudiaf_Parameter-Free_Online_Test-Time_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Boudiaf_Parameter-Free_Online_Test-Time_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
SIGMA: Semantic-Complete Graph Matching for Domain Adaptive Object Detection | Wuyang Li, Xinyu Liu, Yixuan Yuan | Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-conditional distributions by narrowing down cross-domain prototypes (class centers). Though great success, they ignore the significant within-class variance and the domain-mismatched semantics within the training batch, leading to a sub-optimal adaptation. To overcome these challenges, we propose a novel SemantIc-complete Graph MAtching (SIGMA) framework for DAOD, which completes mismatched semantics and reformulates the adaptation with graph matching. Specifically, we design a Graph-embedded Semantic Completion module (GSC) that completes mismatched semantics through generating hallucination graph nodes in missing categories. Then, we establish cross-image graphs to model class-conditional distributions and learn a graph-guided memory bank for better semantic completion in turn. After representing the source and target data as graphs, we reformulate the adaptation as a graph matching problem, i.e., finding well-matched node pairs across graphs to reduce the domain gap, which is solved with a novel Bipartite Graph Matching adaptor (BGM). In a nutshell, we utilize graph nodes to establish semantic-aware node affinity and leverage graph edges as quadratic constraints in a structure-aware matching loss, achieving fine-grained adaptation with a node-to-node graph matching. Extensive experiments verify that SIGMA outperforms existing works significantly. Our codes are available at https://github.com/CityU-AIM-Group/SIGMA. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_SIGMA_Semantic-Complete_Graph_Matching_for_Domain_Adaptive_Object_Detection_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_SIGMA_Semantic-Complete_Graph_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.06398 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SIGMA_Semantic-Complete_Graph_Matching_for_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_SIGMA_Semantic-Complete_Graph_Matching_for_Domain_Adaptive_Object_Detection_CVPR_2022_paper.html | CVPR 2022 | null |
Global Convergence of MAML and Theory-Inspired Neural Architecture Search for Few-Shot Learning | Haoxiang Wang, Yite Wang, Ruoyu Sun, Bo Li | Model-agnostic meta-learning (MAML) and its variants have become popular approaches for few-shot learning. However, due to the non-convexity of deep neural nets (DNNs) and the bi-level formulation of MAML, the theoretical properties of MAML with DNNs remain largely unknown. In this paper, we first prove that MAML with over-parameterized DNNs is guaranteed to converge to global optima at a linear rate. Our convergence analysis indicates that MAML with over-parameterized DNNs is equivalent to kernel regression with a novel class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then, we propose MetaNTK-NAS, a new training-free neural architecture search (NAS) method for few-shot learning that uses MetaNTK to rank and select architectures. Empirically, we compare our MetaNTK-NAS with previous NAS methods on two popular few-shot learning benchmarks, miniImageNet, and tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or better than the state-of-the-art NAS method designed for few-shot learning while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS makes itself more practical for many real-world tasks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Global_Convergence_of_MAML_and_Theory-Inspired_Neural_Architecture_Search_for_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Global_Convergence_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.09137 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Global_Convergence_of_MAML_and_Theory-Inspired_Neural_Architecture_Search_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Global_Convergence_of_MAML_and_Theory-Inspired_Neural_Architecture_Search_for_CVPR_2022_paper.html | CVPR 2022 | null |
LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypoints | Junshu Tang, Zhijun Gong, Ran Yi, Yuan Xie, Lizhuang Ma | Point cloud completion aims at completing geometric and topological shapes from a partial observation. However, some topology of the original shape is missing, existing methods directly predict the location of complete points, without predicting structured and topological information of the complete shape, which leads to inferior performance. To better tackle the missing topology part, we propose LAKe-Net, a novel topology-aware point cloud completion model by localizing aligned keypoints, with a novel Keypoints-Skeleton-Shape prediction manner. Specifically, our method completes missing topology using three steps: Aligned Keypoint Localization. An asymmetric keypoint locator, including an unsupervised multi-scale keypoint detector and a complete keypoint generator, is proposed for localizing aligned keypoints from complete and partial point clouds. We theoretically prove that the detector can capture aligned keypoints for objects within a sub-category. Surface-skeleton Generation. A new type of skeleton, named Surface-skeleton, is generated from keypoints based on geometric priors to fully represent the topological information captured from keypoints and better recover the local details. Shape Refinement. We design a refinement subnet where multi-scale surface-skeletons are fed into each recursive skeleton-assisted refinement module to assist the completion process. Experimental results show that our method achieves the state-of-the-art performance on point cloud completion. | https://openaccess.thecvf.com/content/CVPR2022/papers/Tang_LAKe-Net_Topology-Aware_Point_Cloud_Completion_by_Localizing_Aligned_Keypoints_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_LAKe-Net_Topology-Aware_Point_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_LAKe-Net_Topology-Aware_Point_Cloud_Completion_by_Localizing_Aligned_Keypoints_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Tang_LAKe-Net_Topology-Aware_Point_Cloud_Completion_by_Localizing_Aligned_Keypoints_CVPR_2022_paper.html | CVPR 2022 | null |
Scribble-Supervised LiDAR Semantic Segmentation | Ozan Unal, Dengxin Dai, Luc Van Gool | Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised performance, developing efficient methods that take advantage of realistic weak supervision have yet to be explored. In this paper, we propose using scribbles to annotate LiDAR point clouds and release ScribbleKITTI, the first scribble-annotated dataset for LiDAR semantic segmentation. Furthermore, we present a pipeline to reduce the performance gap that arises when using such weak annotations. Our pipeline comprises of three stand-alone contributions that can be combined with any LiDAR semantic segmentation model to achieve up to 95.7% of the fully-supervised performance while using only 8% labeled points. Our scribble annotations and code are available at github.com/ouenal/scribblekitti. | https://openaccess.thecvf.com/content/CVPR2022/papers/Unal_Scribble-Supervised_LiDAR_Semantic_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Unal_Scribble-Supervised_LiDAR_Semantic_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.08537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Unal_Scribble-Supervised_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Unal_Scribble-Supervised_LiDAR_Semantic_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
AlignMixup: Improving Representations by Interpolating Aligned Features | Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis | Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels. However, how to best interpolate images is not well defined. Recent mixup methods overlay or cut-and-paste two or more objects into one image, which needs care in selecting regions. Mixup has also been connected to autoencoders, because often autoencoders generate an image that continuously deforms into another. However, such images are typically of low quality. In this work, we revisit mixup from the deformation perspective and introduce AlignMixup, where we geometrically align two images in the feature space. The correspondences allow us to interpolate between two sets of features, while keeping the locations of one set. Interestingly, this retains mostly the geometry or pose of one image and the appearance or texture of the other. We also show that an autoencoder can still improve representation learning under mixup, without the classifier ever seeing decoded images. AlignMixup outperforms state-of-the-art mixup methods on five different benchmarks. | https://openaccess.thecvf.com/content/CVPR2022/papers/Venkataramanan_AlignMixup_Improving_Representations_by_Interpolating_Aligned_Features_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Venkataramanan_AlignMixup_Improving_Representations_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2103.15375 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Venkataramanan_AlignMixup_Improving_Representations_by_Interpolating_Aligned_Features_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Venkataramanan_AlignMixup_Improving_Representations_by_Interpolating_Aligned_Features_CVPR_2022_paper.html | CVPR 2022 | null |
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences With Static Models by Fitting Feature-Level Space-Time Surfaces | Jia-Xing Zhong, Kaichen Zhou, Qingyong Hu, Bing Wang, Niki Trigoni, Andrew Markham | Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification since the unstructured points make it hard or even impossible to efficiently and effectively trace point-wise correspondences. To capture 3D motions without explicitly tracking correspondences, we propose a kinematics-inspired neural network (Kinet) by generalizing the kinematic concept of ST-surfaces to the feature space. By unrolling the normal solver of ST-surfaces in the feature space, Kinet implicitly encodes feature-level dynamics and gains advantages from the use of mature backbones for static point cloud processing. With only minor changes in network structures and low computing overhead, it is painless to jointly train and deploy our framework with a given static model. Experiments on NvGesture, SHREC'17, MSRAction-3D, and NTU-RGBD demonstrate its efficacy in performance, efficiency in both the number of parameters and computational complexity, as well as its versatility to various static backbones. Noticeably, Kinet achieves the accuracy of 93.27% on MSRAction-3D with only 3.20M parameters and 10.35G FLOPS. The code is available at https://github.com/jx-zhong-for-academic-purpose/Kinet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhong_No_Pain_Big_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.11113 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.html | CVPR 2022 | null |
HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction | Zikang Zhou, Luyao Ye, Jianping Wang, Kui Wu, Kejie Lu | Accurately predicting the future motions of surrounding traffic agents is critical for the safety of autonomous vehicles. Recently, vectorized approaches have dominated the motion prediction community due to their capability of capturing complex interactions in traffic scenes. However, existing methods neglect the symmetries of the problem and suffer from the expensive computational cost, facing the challenge of making real-time multi-agent motion prediction without sacrificing the prediction performance. To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction. By decomposing the problem into local context extraction and global interaction modeling, our method can effectively and efficiently model a large number of agents in the scene. Meanwhile, we propose a translation-invariant scene representation and rotation-invariant spatial learning modules, which extract features robust to the geometric transformations of the scene and enable the model to make accurate predictions for multiple agents in a single forward pass. Experiments show that HiVT achieves the state-of-the-art performance on the Argoverse motion forecasting benchmark with a small model size and can make fast multi-agent motion prediction. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhou_HiVT_Hierarchical_Vector_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhou_HiVT_Hierarchical_Vector_Transformer_for_Multi-Agent_Motion_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
HerosNet: Hyperspectral Explicable Reconstruction and Optimal Sampling Deep Network for Snapshot Compressive Imaging | Xuanyu Zhang, Yongbing Zhang, Ruiqin Xiong, Qilin Sun, Jian Zhang | Hyperspectral imaging is an essential imaging modality for a wide range of applications, especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral cameras that are either slow, expensive, or bulky, reconstructing hyperspectral images (HSIs) from a low-budget snapshot measurement has drawn wide attention. By mapping a truncated numerical optimization algorithm into a network with a fixed number of phases, recent deep unfolding networks (DUNs) for spectral snapshot compressive sensing (SCI) have achieved remarkable success. However, DUNs are far from reaching the scope of industrial applications limited by the lack of cross-phase feature interaction and adaptive parameter adjustment. In this paper, we propose a novel Hyperspectral Explicable Reconstruction and Optimal Sampling deep Network for SCI, dubbed HerosNet, which includes several phases under the ISTA-unfolding framework. Each phase can flexibly simulate the sensing matrix and contextually adjust the step size in the gradient descent step, and hierarchically fuse and interact the hidden states of previous phases to effectively recover current HSI frames in the proximal mapping step. Simultaneously, a hardware-friendly optimal binary mask is learned end-to-end to further improve the reconstruction performance. Finally, our HerosNet is validated to outperform the state-of-the-art methods on both simulation and real datasets by large margins. The source code is available at https://github.com/jianzhangcs/HerosNet. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_HerosNet_Hyperspectral_Explicable_Reconstruction_and_Optimal_Sampling_Deep_Network_for_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2112.06238 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_HerosNet_Hyperspectral_Explicable_Reconstruction_and_Optimal_Sampling_Deep_Network_for_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_HerosNet_Hyperspectral_Explicable_Reconstruction_and_Optimal_Sampling_Deep_Network_for_CVPR_2022_paper.html | CVPR 2022 | null |
Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space | Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric P. Xing | This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search a sub-structure from the original model end-to-end across multiple dimensions, including the input tokens, MHSA and MLP modules with state-of-the-art performance. Our method is based on a learnable and unified l1 sparsity constraint with pre-defined factors to reflect the global importance in the continuous searching space of different dimensions. The searching process is highly efficient through a single-shot training scheme. For instance, on DeiT-S, ViT-Slim only takes 43 GPU hours for the searching process, and the searched structure is flexible with diverse dimensionalities in different modules. Then, a budget threshold is employed according to the requirements of accuracy-FLOPs trade-off on running devices, and a re-training process is performed to obtain the final model. The extensive experiments show that our ViT-Slim can compress up to 40% of parameters and 40% FLOPs on various vision transformers while increasing the accuracy by 0.6% on ImageNet. We also demonstrate the advantage of our searched models on several downstream datasets. Our code is available at https://github.com/Arnav0400/ViT-Slim. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chavan_Vision_Transformer_Slimming_Multi-Dimension_Searching_in_Continuous_Optimization_Space_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2201.00814 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chavan_Vision_Transformer_Slimming_Multi-Dimension_Searching_in_Continuous_Optimization_Space_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chavan_Vision_Transformer_Slimming_Multi-Dimension_Searching_in_Continuous_Optimization_Space_CVPR_2022_paper.html | CVPR 2022 | null |
Brain-Inspired Multilayer Perceptron With Spiking Neurons | Wenshuo Li, Hanting Chen, Jianyuan Guo, Ziyang Zhang, Yunhe Wang | Recently, Multilayer Perceptron (MLP) becomes the hotspot in the field of computer vision tasks. Without inductive bias, MLPs perform well on feature extraction and achieve amazing results. However, due to the simplicity of their structures, the performance highly depends on the local features communication machenism. To further improve the performance of MLP, we introduce information communication mechanisms from brain-inspired neural networks. Spiking Neural Network (SNN) is the most famous brain-inspired neural network, and achieve great success on dealing with sparse data. Leaky Integrate and Fire (LIF) neurons in SNNs are used to communicate between different time steps. In this paper, we incorporate the machanism of LIF neurons into the MLP models, to achieve better accuracy without extra FLOPs. We propose a full-precision LIF operation to communicate between patches, including horizontal LIF and vertical LIF in different directions. We also propose to use group LIF to extract better local features. With LIF modules, our SNN-MLP model achieves 81.9%, 83.3% and 83.5% top-1 accuracy on ImageNet dataset with only 4.4G, 8.5G and 15.2G FLOPs, respectively, which are state-of-the-art results as far as we know. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Brain-Inspired_Multilayer_Perceptron_With_Spiking_Neurons_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.14679 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Brain-Inspired_Multilayer_Perceptron_With_Spiking_Neurons_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Brain-Inspired_Multilayer_Perceptron_With_Spiking_Neurons_CVPR_2022_paper.html | CVPR 2022 | null |
Learning To Estimate Robust 3D Human Mesh From In-the-Wild Crowded Scenes | Hongsuk Choi, Gyeongsik Moon, JoonKyu Park, Kyoung Mu Lee | We consider the problem of recovering a single person's 3D human mesh from in-the-wild crowded scenes. While much progress has been in 3D human mesh estimation, existing methods struggle when test input has crowded scenes. The first reason for the failure is a domain gap between training and testing data. A motion capture dataset, which provides accurate 3D labels for training, lacks crowd data and impedes a network from learning crowded scene-robust image features of a target person. The second reason is a feature processing that spatially averages the feature map of a localized bounding box containing multiple people. Averaging the whole feature map makes a target person's feature indistinguishable from others. We present 3DCrowdNet that firstly explicitly targets in-the-wild crowded scenes and estimates a robust 3D human mesh by addressing the above issues. First, we leverage 2D human pose estimation that does not require a motion capture dataset with 3D labels for training and does not suffer from the domain gap. Second, we propose a joint-based regressor that distinguishes a target person's feature from others. Our joint-based regressor preserves the spatial activation of a target by sampling features from the target's joint locations and regresses human model parameters. As a result, 3DCrowdNet learns target-focused features and effectively excludes the irrelevant features of nearby persons. We conduct experiments on various benchmarks and prove the robustness of 3DCrowdNet to the in-the-wild crowded scenes both quantitatively and qualitatively. Codes are available here: https://github.com/hongsukchoi/3DCrowdNet_RELEASE | https://openaccess.thecvf.com/content/CVPR2022/papers/Choi_Learning_To_Estimate_Robust_3D_Human_Mesh_From_In-the-Wild_Crowded_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Choi_Learning_To_Estimate_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.07300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Learning_To_Estimate_Robust_3D_Human_Mesh_From_In-the-Wild_Crowded_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Choi_Learning_To_Estimate_Robust_3D_Human_Mesh_From_In-the-Wild_Crowded_CVPR_2022_paper.html | CVPR 2022 | null |
ObjectFormer for Image Manipulation Detection and Localization | Junke Wang, Zuxuan Wu, Jingjing Chen, Xintong Han, Abhinav Shrivastava, Ser-Nam Lim, Yu-Gang Jiang | Recent advances in image editing techniques have posed serious challenges to the trustworthiness of multimedia data, which drives the research of image tampering detection. In this paper, we propose ObjectFormer to detect and localize image manipulations. To capture subtle manipulation traces that are no longer visible in the RGB domain, we extract the high-frequency features of the images and combine them with RGB features as multimodal patch embeddings. In order to detect forgery traces, we use a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions, which are further used to refine patch embeddings to capture the patch-level consistencies. We conduct extensive experiments on various datasets and the results verify the effectiveness of the proposed method, outperforming state-of-the-art tampering detection and localization methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_ObjectFormer_for_Image_Manipulation_Detection_and_Localization_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2203.14681 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ObjectFormer_for_Image_Manipulation_Detection_and_Localization_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ObjectFormer_for_Image_Manipulation_Detection_and_Localization_CVPR_2022_paper.html | CVPR 2022 | null |
Detecting Deepfakes With Self-Blended Images | Kaede Shiohara, Toshihiko Yamasaki | In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images, reproducing common forgery artifacts (e.g., blending boundaries and statistical inconsistencies between source and target images). The key idea behind SBIs is that more general and hardly recognizable fake samples encourage classifiers to learn generic and robust representations without overfitting to manipulation-specific artifacts. We compare our approach with state-of- the-art methods on FF++, CDF, DFD, DFDC, DFDCP, and FFIW datasets by following the standard cross-dataset and cross-manipulation protocols. Extensive experiments show that our method improves the model generalization to unknown manipulations and scenes. In particular, on DFDC and DFDCP where existing methods suffer from the domain gap between the training and test sets, our approach outperforms the baseline by 4.90% and 11.78% points in the cross-dataset evaluation, respectively. Code is available at https://github.com/mapooon/SelfBlendedImages. | https://openaccess.thecvf.com/content/CVPR2022/papers/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Shiohara_Detecting_Deepfakes_With_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.08376 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Shiohara_Detecting_Deepfakes_With_Self-Blended_Images_CVPR_2022_paper.html | CVPR 2022 | null |
Correlation-Aware Deep Tracking | Fei Xie, Chunyu Wang, Guangting Wang, Yue Cao, Wankou Yang, Wenjun Zeng | Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-like networks can not fully discriminatively model the tracked targets and distractor objects, hindering them from simultaneously meeting these two requirements. While most methods focus on designing robust matching operations, we propose a novel target-dependent feature network inspired by the self-/cross-attention scheme. In contrast to the Siamese-like feature extraction, our network deeply embeds cross-image feature correlation in multiple layers of the feature network. By extensively matching the features of the two images through multiple layers, it is able to suppress non-target features, resulting in instance-varying feature extraction. The output features of the search image can be directly used for predicting target locations without extra correlation step. Moreover, our model can be flexibly pre-trained on abundant unpaired images, leading to notably faster convergence than the existing methods. Extensive experiments show our method achieves the state-of-the-art results while running at real-time. Our feature networks also can be applied to existing tracking pipelines seamlessly to raise the tracking performance. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xie_Correlation-Aware_Deep_Tracking_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xie_Correlation-Aware_Deep_Tracking_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.01666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Correlation-Aware_Deep_Tracking_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xie_Correlation-Aware_Deep_Tracking_CVPR_2022_paper.html | CVPR 2022 | null |
Learnable Irrelevant Modality Dropout for Multimodal Action Recognition on Modality-Specific Annotated Videos | Saghir Alfasly, Jian Lu, Chen Xu, Yuru Zou | With the assumption that a video dataset is multimodality annotated in which auditory and visual modalities both are labeled or class-relevant, current multimodal methods apply modality fusion or cross-modality attention. However, effectively leveraging the audio modality in vision-specific annotated videos for action recognition is of particular challenge. To tackle this challenge, we propose a novel audio-visual framework that effectively leverages the audio modality in any solely vision-specific annotated dataset. We adopt the language models (e.g., BERT) to build a semantic audio-video label dictionary (SAVLD) that maps each video label to its most K-relevant audio labels in which SAVLD serves as a bridge between audio and video datasets. Then, SAVLD along with a pretrained audio multi-label model are used to estimate the audio-visual modality relevance during the training phase. Accordingly, a novel learnable irrelevant modality dropout (IMD) is proposed to completely drop out the irrelevant audio modality and fuse only the relevant modalities. Moreover, we present a new two-stream video Transformer for efficiently modeling the visual modalities. Results on several vision-specific annotated datasets including Kinetics400 and UCF-101 validated our framework as it outperforms most relevant action recognition methods. | https://openaccess.thecvf.com/content/CVPR2022/papers/Alfasly_Learnable_Irrelevant_Modality_Dropout_for_Multimodal_Action_Recognition_on_Modality-Specific_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Alfasly_Learnable_Irrelevant_Modality_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.03014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Alfasly_Learnable_Irrelevant_Modality_Dropout_for_Multimodal_Action_Recognition_on_Modality-Specific_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Alfasly_Learnable_Irrelevant_Modality_Dropout_for_Multimodal_Action_Recognition_on_Modality-Specific_CVPR_2022_paper.html | CVPR 2022 | null |
NeurMiPs: Neural Mixture of Planar Experts for View Synthesis | Zhi-Hao Lin, Wei-Chiu Ma, Hao-Yu Hsu, Yu-Chiang Frank Wang, Shenlong Wang | We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based scene representation for modeling geometry and appearance. NeurMiPs leverages a collection of local planar experts in 3D space as the scene representation. Each planar expert consists of the parameters of the local rectangular shape representing geometry and a neural radiance field modeling the color and opacity. We render novel views by calculating ray-plane intersections and composite output colors and densities at intersected points to the image. NeurMiPs blends the efficiency of explicit mesh rendering and flexibility of the neural radiance field. Experiments demonstrate that our proposed method achieves superior performance and speedup compared to other 3D representations for novel view synthesis. | https://openaccess.thecvf.com/content/CVPR2022/papers/Lin_NeurMiPs_Neural_Mixture_of_Planar_Experts_for_View_Synthesis_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Lin_NeurMiPs_Neural_Mixture_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_NeurMiPs_Neural_Mixture_of_Planar_Experts_for_View_Synthesis_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Lin_NeurMiPs_Neural_Mixture_of_Planar_Experts_for_View_Synthesis_CVPR_2022_paper.html | CVPR 2022 | null |
Implicit Sample Extension for Unsupervised Person Re-Identification | Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang | Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different true identities together or splits the same identity into two or more sub clusters. Training on these noisy clusters substantially hampers the Re-ID accuracy. Due to the limited samples in each identity, we suppose there may lack some underlying information to well reveal the accurate clusters. To discover these information, we propose an Implicit Sample Extension (ISE) method to generate what we call support samples around the cluster boundaries. Specifically, we generate support samples from actual samples and their neighbouring clusters in the embedding space through a progressive linear interpolation (PLI) strategy. PLI controls the generation with two critical factors, i.e., 1) the direction from the actual sample towards its K-nearest clusters and 2) the degree for mixing up the context information from the K-nearest clusters. Meanwhile, given the support samples, ISE further uses a label-preserving loss to pull them towards their corresponding actual samples, so as to compact each cluster. Consequently, ISE reduces the "sub and mixed" clustering errors, thus improving the Re-ID performance. Extensive experiments demonstrate that the proposed method is effective and achieves state-of-the-art performance for unsupervised person Re-ID. Code is available at: https://github.com/PaddlePaddle/PaddleClas. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.pdf | null | http://arxiv.org/abs/2204.06892 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Implicit_Sample_Extension_for_Unsupervised_Person_Re-Identification_CVPR_2022_paper.html | CVPR 2022 | null |
Energy-Based Latent Aligner for Incremental Learning | K J Joseph, Salman Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, Vineeth N Balasubramanian | Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks may not align well with the updates suitable for older tasks. The resulting latent representation mismatch causes forgetting. In this work, we propose ELI: Energy-based Latent Aligner for Incremental Learning, which first learns an energy manifold for the latent representations such that previous task latents will have low energy and the current task latents have high energy values. This learned manifold is used to counter the representational shift that happens during incremental learning. The implicit regularization that is offered by our proposed methodology can be used as a plug-and-play module in existing incremental learning methodologies. We validate this through extensive evaluation on CIFAR-100, ImageNet subset, ImageNet 1k and Pascal VOC datasets. We observe consistent improvement when ELI is added to three prominent methodologies in class-incremental learning, across multiple incremental settings. Further, when added to the state-of-the-art incremental object detector, ELI provides over 5% improvement in detection accuracy, corroborating its effectiveness and complementary advantage to existing art. Code is available at: https://github.com/JosephKJ/ELI. | https://openaccess.thecvf.com/content/CVPR2022/papers/Joseph_Energy-Based_Latent_Aligner_for_Incremental_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Joseph_Energy-Based_Latent_Aligner_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.14952 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Joseph_Energy-Based_Latent_Aligner_for_Incremental_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Joseph_Energy-Based_Latent_Aligner_for_Incremental_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Towards Semi-Supervised Deep Facial Expression Recognition With an Adaptive Confidence Margin | Hangyu Li, Nannan Wang, Xi Yang, Xiaoyu Wang, Xinbo Gao | Only parts of unlabeled data are selected to train models for most semi-supervised learning methods, whose confidence scores are usually higher than the pre-defined threshold (i.e., the confidence margin). We argue that the recognition performance should be further improved by making full use of all unlabeled data. In this paper, we learn an Adaptive Confidence Margin (Ada-CM) to fully leverage all unlabeled data for semi-supervised deep facial expression recognition. All unlabeled samples are partitioned into two subsets by comparing their confidence scores with the adaptively learned confidence margin at each training epoch: (1) subset I including samples whose confidence scores are no lower than the margin; (2) subset II including samples whose confidence scores are lower than the margin. For samples in subset I, we constrain their predictions to match pseudo labels. Meanwhile, samples in subset II participate in the feature-level contrastive objective to learn effective facial expression features. We extensively evaluate Ada-CM on four challenging datasets, showing that our method achieves state-of-the-art performance, especially surpassing fully-supervised baselines in a semi-supervised manner. Ablation study further proves the effectiveness of our method. The source code is available at https://github.com/hangyu94/Ada-CM. | https://openaccess.thecvf.com/content/CVPR2022/papers/Li_Towards_Semi-Supervised_Deep_Facial_Expression_Recognition_With_an_Adaptive_Confidence_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Towards_Semi-Supervised_Deep_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.12341 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_Semi-Supervised_Deep_Facial_Expression_Recognition_With_an_Adaptive_Confidence_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Li_Towards_Semi-Supervised_Deep_Facial_Expression_Recognition_With_an_Adaptive_Confidence_CVPR_2022_paper.html | CVPR 2022 | null |
GanOrCon: Are Generative Models Useful for Few-Shot Segmentation? | Oindrila Saha, Zezhou Cheng, Subhransu Maji | Advances in generative modeling based on GANs has motivated the community to find their use beyond image generation and editing tasks. In particular, several recent works have shown that GAN representations can be re-purposed for discriminative tasks such as part segmentation, especially when training data is limited. But how do these improvements stack-up against recent advances in self-supervised learning? Motivated by this we present an alternative approach based on contrastive learning and compare their performance on standard few-shot part segmentation benchmarks. Our experiments reveal that not only do the GAN-based approach offer no significant performance advantage, their multi-step training is complex, nearly an order-of-magnitude slower, and can introduce additional bias. These experiments suggest that the inductive biases of generative models, such as their ability to disentangle shape and texture, are well captured by standard feed-forward networks trained using contrastive learning. | https://openaccess.thecvf.com/content/CVPR2022/papers/Saha_GanOrCon_Are_Generative_Models_Useful_for_Few-Shot_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Saha_GanOrCon_Are_Generative_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.00854 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Saha_GanOrCon_Are_Generative_Models_Useful_for_Few-Shot_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Saha_GanOrCon_Are_Generative_Models_Useful_for_Few-Shot_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Bi-Level Doubly Variational Learning for Energy-Based Latent Variable Models | Ge Kan, Jinhu Lü, Tian Wang, Baochang Zhang, Aichun Zhu, Lei Huang, Guodong Guo, Hichem Snoussi | Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection. | https://openaccess.thecvf.com/content/CVPR2022/papers/Kan_Bi-Level_Doubly_Variational_Learning_for_Energy-Based_Latent_Variable_Models_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Kan_Bi-Level_Doubly_Variational_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Kan_Bi-Level_Doubly_Variational_Learning_for_Energy-Based_Latent_Variable_Models_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Kan_Bi-Level_Doubly_Variational_Learning_for_Energy-Based_Latent_Variable_Models_CVPR_2022_paper.html | CVPR 2022 | null |
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems | Xin Dong, Barbara De Salvo, Meng Li, Chiao Liu, Zhongnan Qu, H.T. Kung, Ziyun Li | We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head-mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints. To achieve an optimal balance among computation, communication, and performance, a split-aware neural architecture search framework, SplitNets, is introduced to conduct model designing, splitting, and communication reduction simultaneously. We further extend the framework to multi-view systems for learning to fuse inputs from multiple camera sensors with optimal performance and systemic efficiency. We validate SplitNets for single-view system on ImageNet as well as multi-view system on 3D classification, and show that the SplitNets framework achieves state-of-the-art (SOTA) performance and system latency compared with existing approaches. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dong_SplitNets_Designing_Neural_Architectures_for_Efficient_Distributed_Computing_on_Head-Mounted_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dong_SplitNets_Designing_Neural_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.04705 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_SplitNets_Designing_Neural_Architectures_for_Efficient_Distributed_Computing_on_Head-Mounted_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dong_SplitNets_Designing_Neural_Architectures_for_Efficient_Distributed_Computing_on_Head-Mounted_CVPR_2022_paper.html | CVPR 2022 | null |
Masked-Attention Mask Transformer for Universal Image Segmentation | Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar | Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K). | https://openaccess.thecvf.com/content/CVPR2022/papers/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Cheng_Masked-Attention_Mask_Transformer_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.01527 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Masked-Attention_Mask_Transformer_for_Universal_Image_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Reading To Listen at the Cocktail Party: Multi-Modal Speech Separation | Akam Rahimi, Triantafyllos Afouras, Andrew Zisserman | The goal of this paper is speech separation and enhancement in multi-speaker and noisy environments using a combination of different modalities. Previous works have shown good performance when conditioning on temporal or static visual evidence such as synchronised lip movements or face identity. In this paper we present a unified framework for multi-modal speech separation and enhancement based on synchronous or asynchronous cues. To that end we make the following contributions: (i) we design a modern Transformer-based architecture which inputs and outputs raw waveforms and is tailored to fuse different modalities to solve the speech separation task; (ii) we propose conditioning on the text content of a sentence alone or in combination with visual information; (iii) we demonstrate the robustness of our model to audio-visual synchronisation offsets; and, (iv) we obtain state-of-the art performance on the well-established benchmark datasets LRS2 and LRS3. | https://openaccess.thecvf.com/content/CVPR2022/papers/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Rahimi_Reading_To_Listen_at_the_Cocktail_Party_Multi-Modal_Speech_Separation_CVPR_2022_paper.html | CVPR 2022 | null |
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval | Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Tetsuya Sakai | Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applications where conventional measures are not well suited. Video Moment Retrieval (VMR) is one such application, and the current practice is to use R@K,\theta for evaluating VMR systems. However, this measure has two disadvantages. First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-K ranked list by treating the list as a set. Second, it binarises the Intersection over Union (IoU) of each retrieved video moment using the threshold \theta and thereby ignoring fine-grained localisation quality of ranked moments. We propose an alternative measure for evaluating VMR, called Average Max IoU (AxIoU), which is free from the above two problems. We show that AxIoU satisfies two important axioms for VMR evaluation, namely, Invariance against Redundant Moments and Monotonicity with respect to the Best Moment, and also that R@K,\theta satisfies the first axiom only. We also empirically examine how AxIoU agrees with R@K,\theta, as well as its stability with respect to change in the test data and human-annotated temporal boundaries. | https://openaccess.thecvf.com/content/CVPR2022/papers/Togashi_AxIoU_An_Axiomatically_Justified_Measure_for_Video_Moment_Retrieval_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Togashi_AxIoU_An_Axiomatically_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2203.16062 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Togashi_AxIoU_An_Axiomatically_Justified_Measure_for_Video_Moment_Retrieval_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Togashi_AxIoU_An_Axiomatically_Justified_Measure_for_Video_Moment_Retrieval_CVPR_2022_paper.html | CVPR 2022 | null |
NOC-REK: Novel Object Captioning With Retrieved Vocabulary From External Knowledge | Duc Minh Vo, Hong Chen, Akihiro Sugimoto, Hideki Nakayama | Novel object captioning aims at describing objects absent from training data, with the key ingredient being the provision of object vocabulary to the model. Although existing methods heavily rely on an object detection model, we view the detection step as vocabulary retrieval from an external knowledge in the form of embeddings for any object's definition from Wiktionary, where we use in the retrieval image region features learned from a transformers model. We propose an end-to-end Novel Object Captioning with Retrieved vocabulary from External Knowledge method (NOC-REK), which simultaneously learns vocabulary retrieval and caption generation, successfully describing novel objects outside of the training dataset. Furthermore, our model eliminates the requirement for model retraining by simply updating the external knowledge whenever a novel object appears. Our comprehensive experiments on held-out COCO and Nocaps datasets show that our NOC-REK is considerably effective against SOTAs. | https://openaccess.thecvf.com/content/CVPR2022/papers/Vo_NOC-REK_Novel_Object_Captioning_With_Retrieved_Vocabulary_From_External_Knowledge_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Vo_NOC-REK_Novel_Object_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Vo_NOC-REK_Novel_Object_Captioning_With_Retrieved_Vocabulary_From_External_Knowledge_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Vo_NOC-REK_Novel_Object_Captioning_With_Retrieved_Vocabulary_From_External_Knowledge_CVPR_2022_paper.html | CVPR 2022 | null |
Boosting Robustness of Image Matting With Context Assembling and Strong Data Augmentation | Yutong Dai, Brian Price, He Zhang, Chunhua Shen | Deep image matting methods have achieved increasingly better results on benchmarks (e.g., Composition-1k/alphamatting.com). However, the robustness, including robustness to trimaps and generalization to images from different domains, is still under-explored. Although some works propose to either refine the trimaps or adapt the algorithms to real-world images via extra data augmentation, none of them has taken both into consideration, not to mention the significant performance deterioration on benchmarks while using those data augmentation. To fill this gap, we propose an image matting method which achieves higher robustness (RMat) via multilevel context assembling and strong data augmentation targeting matting. Specifically, we first build a strong matting framework by modeling ample global information with transformer blocks in the encoder, and focusing on details in combination with convolution layers as well as a low-level feature assembling attention block in the decoder. Then, based on this strong baseline, we analyze current data augmentation and explore simple but effective strong data augmentation to boost the baseline model and contribute a more generalizable matting method. Compared with previous methods, the proposed method not only achieves state-of-the-art results on the Composition-1k benchmark (11% improvement on SAD and 27% improvement on Grad) with smaller model size, but also shows more robust generalization results on other benchmarks, on real-world images, and also on varying coarse-to-fine trimaps with our extensive experiments. | https://openaccess.thecvf.com/content/CVPR2022/papers/Dai_Boosting_Robustness_of_Image_Matting_With_Context_Assembling_and_Strong_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Dai_Boosting_Robustness_of_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2201.06889 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_Boosting_Robustness_of_Image_Matting_With_Context_Assembling_and_Strong_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Dai_Boosting_Robustness_of_Image_Matting_With_Context_Assembling_and_Strong_CVPR_2022_paper.html | CVPR 2022 | null |
Group R-CNN for Weakly Semi-Supervised Object Detection With Points | Shilong Zhang, Zhuoran Yu, Liyang Liu, Xinjiang Wang, Aojun Zhou, Kai Chen | We study the problem of weakly semi-supervised object detection with points (WSSOD-P), where the training data is combined by a small set of fully annotated images with bounding boxes and a large set of weakly-labeled images with only a single point annotated for each instance. The core of this task is to train a point-to-box regressor on well-labeled images that can be used to predict credible bounding boxes for each point annotation. We challenge the prior belief that existing CNN-based detectors are not compatible with this task. Based on the classic R-CNN architecture, we propose an effective point-to-box regressor: Group R-CNN. Group R-CNN first uses instance-level proposal grouping to generate a group of proposals for each point annotation and thus can obtain a high recall rate. To better distinguish different instances and improve precision, we propose instance-level proposal assignment to replace the vanilla assignment strategy adopted in original R-CNN methods. As naive instance-level assignment brings converging difficulty, we propose instance-aware representation learning which consists of instance-aware feature enhancement and instance-aware parameter generation to overcome this issue. Comprehensive experiments on the MS-COCO benchmark demonstrate the effectiveness of our method. Specifically, Group R-CNN significantly outperforms the prior method Point DETR by 3.9 mAP with 5% well-labeled images, which is the most challenging scenario. The source code can be found at https://github.com/jshilong/GroupRCNN. | https://openaccess.thecvf.com/content/CVPR2022/papers/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Zhang_Group_R-CNN_for_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Group_R-CNN_for_Weakly_Semi-Supervised_Object_Detection_With_Points_CVPR_2022_paper.html | CVPR 2022 | null |
Weakly-Supervised Action Transition Learning for Stochastic Human Motion Prediction | Wei Mao, Miaomiao Liu, Mathieu Salzmann | We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short motion history. This differs from existing works, which predict motions that either do not respect any specific action category, or follow a single action label. In particular, addressing this task requires tackling two challenges: The transitions between the different actions must be smooth; the length of the predicted motion depends on the action sequence and varies significantly across samples. As we cannot realistically expect training data to cover sufficiently diverse action transitions and motion lengths, we propose an effective training strategy consisting of combining multiple motions from different actions and introducing a weak form of supervision to encourage smooth transitions. We then design a VAE-based model conditioned on both the observed motion and the action label sequence, allowing us to generate multiple plausible future motions of varying length. We illustrate the generality of our approach by exploring its use with two different temporal encoding models, namely RNNs and Transformers. Our approach outperforms baseline models constructed by adapting state-of-the-art single action-conditioned motion generation methods and stochastic human motion prediction approaches to our new task of action-driven stochastic motion prediction. Our code is available at https://github.com/wei-mao-2019/WAT. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mao_Weakly-Supervised_Action_Transition_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2205.15608 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mao_Weakly-Supervised_Action_Transition_Learning_for_Stochastic_Human_Motion_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Speech Driven Tongue Animation | Salvador Medina, Denis Tome, Carsten Stoll, Mark Tiede, Kevin Munhall, Alexander G. Hauptmann, Iain Matthews | Advances in speech driven animation techniques allow the creation of convincing animations for virtual characters solely from audio data. Many existing approaches focus on facial and lip motion and they often do not provide realistic animation of the inner mouth. This paper addresses the problem of speech-driven inner mouth animation. Obtaining performance capture data of the tongue and jaw from video alone is difficult because the inner mouth is only partially observable during speech. In this work, we introduce a large-scale speech and mocap dataset that focuses on capturing tongue, jaw, and lip motion. This dataset enables research using data-driven techniques to generate realistic inner mouth animation from speech. We then propose a deep-learning based method for accurate and generalizable speech to tongue and jaw animation and evaluate several encoder-decoder network architectures and audio feature encoders. We find that recent self-supervised deep learning based audio feature encoders are robust, generalize well to unseen speakers and content, and work best for our task. To demonstrate the practical application of our approach, we show animations on high-quality parametric 3D face models driven by the landmarks generated from our speech-to-tongue animation method. | https://openaccess.thecvf.com/content/CVPR2022/papers/Medina_Speech_Driven_Tongue_Animation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Medina_Speech_Driven_Tongue_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Medina_Speech_Driven_Tongue_Animation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Medina_Speech_Driven_Tongue_Animation_CVPR_2022_paper.html | CVPR 2022 | null |
Hybrid Relation Guided Set Matching for Few-Shot Action Recognition | Xiang Wang, Shiwei Zhang, Zhiwu Qing, Mingqian Tang, Zhengrong Zuo, Changxin Gao, Rong Jin, Nong Sang | Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal alignment strategies. Nevertheless, they are limited in that (a) learning individual features without considering the entire task may lose the most relevant information in the current episode, and (b) these alignment strategies may fail in misaligned instances. To overcome the two limitations, we propose a novel Hybrid Relation guided Set Matching (HyRSM) approach that incorporates two key components: hybrid relation module and set matching metric. The purpose of the hybrid relation module is to learn task-specific embeddings by fully exploiting associated relations within and cross videos in an episode. Built upon the task-specific features, we reformulate distance measure between query and support videos as a set matching problem and further design a bidirectional Mean Hausdorff Metric to improve the resilience to misaligned instances. By this means, the proposed HyRSM can be highly informative and flexible to predict query categories under the few-shot settings. We evaluate HyRSM on six challenging benchmarks, and the experimental results show its superiority over the state-of-the-art methods by a convincing margin. Project page: https://hyrsm-cvpr2022.github.io/. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_Hybrid_Relation_Guided_Set_Matching_for_Few-Shot_Action_Recognition_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wang_Hybrid_Relation_Guided_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2204.13423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Hybrid_Relation_Guided_Set_Matching_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wang_Hybrid_Relation_Guided_Set_Matching_for_Few-Shot_Action_Recognition_CVPR_2022_paper.html | CVPR 2022 | null |
Self-Supervised Spatial Reasoning on Multi-View Line Drawings | Siyuan Xiang, Anbang Yang, Yanfei Xue, Yaoqing Yang, Chen Feng | Spatial reasoning on multi-view line drawings by state-of-the-art supervised deep networks is recently shown with puzzling low performances on the SPARE3D dataset. Based on the fact that self-supervised learning is helpful when a large number of data are available, we propose two self-supervised learning approaches to improve the baseline performance for view consistency reasoning and camera pose reasoning tasks on the SPARE3D dataset. For the first task, we use a self-supervised binary classification network to contrast the line drawing differences between various views of any two similar 3D objects, enabling the trained networks to effectively learn detail-sensitive yet view-invariant line drawing representations of 3D objects. For the second type of task, we propose a self-supervised multi-class classification framework to train a model to select the correct corresponding view from which a line drawing is rendered. Our method is even helpful for the downstream tasks with unseen camera poses. Experiments show that our methods could significantly increase the baseline performance in SPARE3D, while some popular self-supervised learning methods cannot. | https://openaccess.thecvf.com/content/CVPR2022/papers/Xiang_Self-Supervised_Spatial_Reasoning_on_Multi-View_Line_Drawings_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xiang_Self-Supervised_Spatial_Reasoning_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2104.13433 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Xiang_Self-Supervised_Spatial_Reasoning_on_Multi-View_Line_Drawings_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Xiang_Self-Supervised_Spatial_Reasoning_on_Multi-View_Line_Drawings_CVPR_2022_paper.html | CVPR 2022 | null |
Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation | Zihan Ding, Tianrui Hui, Junshi Huang, Xiaoming Wei, Jizhong Han, Si Liu | Referring video object segmentation aims to predict foreground labels for objects referred by natural language expressions in videos. Previous methods either depend on 3D ConvNets or incorporate additional 2D ConvNets as encoders to extract mixed spatial-temporal features. However, these methods suffer from spatial misalignment or false distractors due to delayed and implicit spatial-temporal interaction occurring in the decoding phase. To tackle these limitations, we propose a Language-Bridged Duplex Transfer (LBDT) module which utilizes language as an intermediary bridge to accomplish explicit and adaptive spatial-temporal interaction earlier in the encoding phase. Concretely, cross-modal attention is performed among the temporal encoder, referring words and the spatial encoder to aggregate and transfer language-relevant motion and appearance information. In addition, we also propose a Bilateral Channel Activation (BCA) module in the decoding phase for further denoising and highlighting the spatial-temporal consistent features via channel-wise activation. Extensive experiments show our method achieves new state-of-the-art performances on four popular benchmarks with 6.8% and 6.9% absolute AP gains on A2D Sentences and J-HMDB Sentences respectively, while consuming around 7x less computational overhead. | https://openaccess.thecvf.com/content/CVPR2022/papers/Ding_Language-Bridged_Spatial-Temporal_Interaction_for_Referring_Video_Object_Segmentation_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Language-Bridged_Spatial-Temporal_Interaction_for_Referring_Video_Object_Segmentation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Ding_Language-Bridged_Spatial-Temporal_Interaction_for_Referring_Video_Object_Segmentation_CVPR_2022_paper.html | CVPR 2022 | null |
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic Images | Huisi Wu, Zhaoze Wang, Youyi Song, Lin Yang, Jing Qin | We study the semi-supervised learning problem, using a few labeled data and a large amount of unlabeled data to train the network, by developing a cross-patch dense contrastive learning framework, to segment cellular nuclei in histopathologic images. This task is motivated by the expensive burden on collecting labeled data for histopathologic image segmentation tasks. The key idea of our method is to align features of teacher and student networks, sampled from cross-image in both patch- and pixel-levels, for enforcing the intra-class compactness and inter-class separability of features that as we shown is helpful for extracting valuable knowledge from unlabeled data. We also design a novel optimization framework that combines consistency regularization and entropy minimization techniques, showing good property in eviction of gradient vanishing. We assess the proposed method on two publicly available datasets, and obtain positive results on extensive experiments, outperforming the state-of-the-art methods. Codes are available at https://github.com/zzw-szu/CDCL. | https://openaccess.thecvf.com/content/CVPR2022/papers/Wu_Cross-Patch_Dense_Contrastive_Learning_for_Semi-Supervised_Segmentation_of_Cellular_Nuclei_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Wu_Cross-Patch_Dense_Contrastive_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Cross-Patch_Dense_Contrastive_Learning_for_Semi-Supervised_Segmentation_of_Cellular_Nuclei_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Cross-Patch_Dense_Contrastive_Learning_for_Semi-Supervised_Segmentation_of_Cellular_Nuclei_CVPR_2022_paper.html | CVPR 2022 | null |
Frame-Wise Action Representations for Long Videos via Sequence Contrastive Learning | Minghao Chen, Fangyun Wei, Chong Li, Deng Cai | Prior works on action representation learning mainly focus on designing various architectures to extract the global representations for short video clips. In contrast, many practical applications such as video alignment have strong demand for learning dense representations for long videos. In this paper, we introduce a novel contrastive action representation learning (CARL) framework to learn frame-wise action representations, especially for long videos, in a self-supervised manner. Concretely, we introduce a simple yet efficient video encoder that considers spatio-temporal context to extract frame-wise representations. Inspired by the recent progress of self-supervised learning, we present a novel sequence contrastive loss (SCL) applied on two correlated views obtained through a series of spatio-temporal data augmentations. SCL optimizes the embedding space by minimizing the KL-divergence between the sequence similarity of two augmented views and a prior Gaussian distribution of timestamp distance. Experiments on FineGym, PennAction and Pouring datasets show that our method outperforms previous state-of-the-art by a large margin for downstream fine-grained action classification. Surprisingly, although without training on paired videos, our approach also shows outstanding performance on video alignment and fine-grained frame retrieval tasks. Code and models will be made public. | https://openaccess.thecvf.com/content/CVPR2022/papers/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Chen_Frame-Wise_Action_Representations_CVPR_2022_supplemental.zip | http://arxiv.org/abs/2203.14957 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Chen_Frame-Wise_Action_Representations_for_Long_Videos_via_Sequence_Contrastive_Learning_CVPR_2022_paper.html | CVPR 2022 | null |
Coarse-To-Fine Deep Video Coding With Hyperprior-Guided Mode Prediction | Zhihao Hu, Guo Lu, Jinyang Guo, Shan Liu, Wei Jiang, Dong Xu | The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards like H.264/H.265 for both motion and residual compression. In this work, we first propose a coarse-to-fine (C2F) deep video compression framework for better motion compensation, in which we perform motion estimation, compression and compensation twice in a coarse to fine manner. Our C2F framework can achieve better motion compensation results without significantly increasing bit costs. Observing hyperprior information (i.e., the mean and variance values) from the hyperprior networks contains discriminant statistical information of different patches, we also propose two efficient hyperprior-guided mode prediction methods. Specifically, using hyperprior information as the input, we propose two mode prediction networks to respectively predict the optimal block resolutions for better motion coding and decide whether to skip residual information from each block for better residual coding without introducing additional bit cost while bringing negligible extra computation cost. Comprehensive experimental results demonstrate our proposed C2F video compression framework equipped with the new hyperprior-guided mode prediction methods achieves the state-of-the-art performance on HEVC, UVG and MCL-JCV datasets. | https://openaccess.thecvf.com/content/CVPR2022/papers/Hu_Coarse-To-Fine_Deep_Video_Coding_With_Hyperprior-Guided_Mode_Prediction_CVPR_2022_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Coarse-To-Fine_Deep_Video_Coding_With_Hyperprior-Guided_Mode_Prediction_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Coarse-To-Fine_Deep_Video_Coding_With_Hyperprior-Guided_Mode_Prediction_CVPR_2022_paper.html | CVPR 2022 | null |
Generalized Binary Search Network for Highly-Efficient Multi-View Stereo | Zhenxing Mi, Chang Di, Dan Xu | Multi-view Stereo (MVS) with known camera parameters is essentially a 1D search problem within a valid depth range. Recent deep learning-based MVS methods typically densely sample depth hypotheses in the depth range, and then construct prohibitively memory-consuming 3D cost volumes for depth prediction. Although coarse-to-fine sampling strategies alleviate this overhead issue to a certain extent, the efficiency of MVS is still an open challenge. In this work, we propose a novel method for highly efficient MVS that remarkably decreases the memory footprint, meanwhile clearly advancing state-of-the-art depth prediction performance. We investigate what a search strategy can be reasonably optimal for MVS taking into account of both efficiency and effectiveness. We first formulate MVS as a binary search problem, and accordingly propose a generalized binary search network for MVS. Specifically, in each step, the depth range is split into 2 bins with extra 1 error tolerance bin on both sides. A classification is performed to identify which bin contains the true depth. We also design three mechanisms to respectively handle classification errors, deal with out-of-range samples and decrease the training memory. The new formulation makes our method only sample a very small number of depth hypotheses in each step, which is highly memory efficient, and also greatly facilitates quick training convergence. Experiments on competitive benchmarks show that our method achieves state-of-the-art accuracy with much less memory. Particularly, our method obtains an overall score of 0.289 on DTU dataset and tops the first place on challenging Tanks and Temples advanced dataset among all the learning-based methods. Our code will be released at https://github.com/MiZhenxing/GBi-Net. | https://openaccess.thecvf.com/content/CVPR2022/papers/Mi_Generalized_Binary_Search_Network_for_Highly-Efficient_Multi-View_Stereo_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mi_Generalized_Binary_Search_CVPR_2022_supplemental.pdf | http://arxiv.org/abs/2112.02338 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Mi_Generalized_Binary_Search_Network_for_Highly-Efficient_Multi-View_Stereo_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Mi_Generalized_Binary_Search_Network_for_Highly-Efficient_Multi-View_Stereo_CVPR_2022_paper.html | CVPR 2022 | null |
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation | Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, Fisher Yu | Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at https://www.vis.xyz/shift. | https://openaccess.thecvf.com/content/CVPR2022/papers/Sun_SHIFT_A_Synthetic_Driving_Dataset_for_Continuous_Multi-Task_Domain_Adaptation_CVPR_2022_paper.pdf | https://openaccess.thecvf.com/content/CVPR2022/supplemental/Sun_SHIFT_A_Synthetic_CVPR_2022_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_SHIFT_A_Synthetic_Driving_Dataset_for_Continuous_Multi-Task_Domain_Adaptation_CVPR_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022/html/Sun_SHIFT_A_Synthetic_Driving_Dataset_for_Continuous_Multi-Task_Domain_Adaptation_CVPR_2022_paper.html | CVPR 2022 | null |
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