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JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection

JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ehsanpour, Mahsa Saleh, Fatemeh Savarese, Silvio Reid, Ian Rezatofighi, Hamid Univ Adelaide Adelaide SA Australia Samsung AI Ctr New York NY USA Stanford Univ Stanford CA 94305 USA Monash Univ Melbourne Vic Australia Australian Natl Univ ANU Canberra ACT Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly unbalanced and long-tailed distributed action labels from a stream of sensory data captured from a mobile robot platform remains a significant challenge, not least owing to the lack of a reflective large-scale dataset. In this paper, we introduce JRDB-Act, as an extension of the existing JRDB, which is captured by a social mobile manipulator and reflects a real distribution of human daily-life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labeled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act provides social group annotation, conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group). Each annotated label in JRDB-Act is tagged with the annotators' confidence level which contributes to the development of reliable evaluation strategies. In order to demonstrate how one can effectively utilise such annotations, we develop an end-to-end trainable pipeline to learn and infer these tasks, i.e. individual action and social group detection.

关键词： Visualization Social groups Annotations Pipelines Social robots Robot sensing systems pattern recognition

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Flow Guided Transformable Bottleneck Networks for Motion Retargeting

Flow Guided Transformable Bottleneck Networks for Motion Ret...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Ren, Jian Chai, Menglei Woodford, Oliver J. Olszewski, Kyle Tulyakov, Sergey Snap Inc Santa Monica CA 90405 USA

ISBN: (纸本)9781665445092

Human motion retargeting aims to transfer the motion of one person in a "driving" video or set of images to another person. Existing efforts leverage a long training video from each target person to train a subject-specific motion transfer model. However, the scalability of such methods is limited, as each model can only generate videos for the given target subject, and such training videos are labor-intensive to acquire and process. Few-shot motion transfer techniques, which only require one or a few images from a target, have recently drawn considerable attention. Methods addressing this task generally use either 2D or explicit 3D representations to transfer motion, and in doing so, sacrifice either accurate geometric modeling or the flexibility of an end-to-end learned representation. Inspired by the Transformable Bottleneck Network, which renders novel views and manipulations of rigid objects, we propose an approach based on an implicit volumetric representation of the image content, which can then be spatially manipulated using volumetric flow fields. We address the challenging question of how to aggregate information across different body poses, learning flow fields that allow for combining content from the appropriate regions of input images of highly non-rigid human subjects performing complex motions into a single implicit volumetric representation. This allows us to learn our 3D representation solely from videos of moving people. Armed with both 3D object understanding and end-to-end learned rendering, this categorically novel representation delivers state-of-the-art image generation quality, as shown by our quantitative and qualitative evaluations.

关键词： Training computer vision Three-dimensional displays Image synthesis Scalability Pose estimation Rendering (computer graphics)

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Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values

Polarity Sampling: Quality and Diversity Control of Pre-Trai...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Humayun, Ahmed Imtiaz Balestriero, Randall Baraniuk, Richard Rice Univ Houston TX 77251 USA Meta AI Res Menlo Pk CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present Polarity Sampling, a theoretically justified plugand-play method for controlling the generation quality and diversity of any pre-trained deep generative network (DGN). Leveraging the fact that DGNs are, or can be approximated by, continuous piecewise affine splines, we derive the analytical DGN output space distribution as a function of the product of the DGN's Jacobian singular values raised to a power rho. We dub p the polarity parameter and prove that rho focuses the DGN sampling on the modes (rho < 0) or anti-modes (rho > 0) of the DGN output-space probability distribution. We demonstrate that nonzero polarity values achieve a better precision-recall (qualitydiversity) Pareto frontier than standard methods, such as truncation, for a number of state-of-the-art DGNs. We also present quantitative and qualitative results on the improvement of overall generation quality (e.g., in terms of the Frechet Inception Distance) for a number of state-of-the-art DGNs, including StyleGAN3, BigGAN-deep, NVAE, for different conditional and unconditional image generation tasks. In particular;Polarity Sampling redefines the state-of-the-art for StyleGAN2 on the FFHQ Dataset to HD 2.57, StyleGAN2 on the LSUN Car Dataset to FID 2.27 and Style-GAN3 on the AFHQv2 Dataset to FID 3.95. Colab Demo.

关键词： Training Jacobian matrices Image synthesis Force Probability distribution Generators pattern recognition

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Robust Federated Learning with Noisy and Heterogeneous Clients

Robust Federated Learning with Noisy and Heterogeneous Clien...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Fang, Xiuwen Ye, Mang Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Inst Artificial IntelligenceHubei Key Lab Multim Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Model heterogeneous federated learning is a challenging task since each client independently designs its own model. Due to the annotation difficulty and free-riding participant issue, the local client usually contains unavoidable and varying noises, which cannot be effectively addressed by existing algorithms. This paper starts the first attempt to study a new and challenging robust federated learning problem with noisy and heterogeneous clients. We present a novel solution RHFL (Robust Heterogeneous Federated Learning), which simultaneously handles the label noise and performs federated learning in a single framework. It is featured in three aspects: (1) For the communication between heterogeneous models, we directly align the models feedback by utilizing public data, which does not require additional shared global models for collaboration. (2) For internal label noise, we apply a robust noise-tolerant loss function to reduce the negative effects. (3) For challenging noisy feedback from other participants, we design a novel client confidence re-weighting scheme, which adaptively assigns corresponding weights to each client in the collaborative learning stage. Extensive experiments validate the effectiveness of our approach in reducing the negative effects of different noise rates/types under both model homogeneous and heterogeneous federated learning settings, consistently outperforming existing methods.

关键词： Adaptation models Privacy computer vision Computational modeling Collaboration Collaborative work Data models

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Learning Non-target Knowledge for Few-shot Semantic Segmentation

Learning Non-target Knowledge for Few-shot Semantic Segmenta...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Yuanwei Liu, Nian Cao, Qinglong Yao, Xiwen Han, Junwei Shao, Ling Northwestern Polytech Univ Xian Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Terminus Grp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in nontarget regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5(i) and COCO-20(i) datasets show that our approach is effective despite its simplicity. Code is available at https://***/LIUYANWEI98/NERTNet

关键词： computer vision Codes Shape Semantics Prototypes Object segmentation Filtering algorithms

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Prototype Augmentation and Self-Supervision for Incremental Learning

Prototype Augmentation and Self-Supervision for Incremental ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhu, Fei Zhang, Xu-Yao Wang, Chuang Yin, Fei Liu, Cheng-Lin Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781665445092

Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally. Recently, various incremental learning methods have been proposed, and some approaches achieved acceptable performance relying on stored data or complex generative models. However, storing data from previous tasks is limited by memory or privacy issues, and generative models are usually unstable and inefficient in training. In this paper, we propose a simple non-exemplar based method named PASS, to address the catastrophic forgetting problem in incremental learning. On the one hand, we propose to memorize one class-representative prototype for each old class and adopt prototype augmentation (protoAug) in the deep feature space to maintain the decision boundary of previous tasks. On the other hand, we employ self:supervised learning (SSL) to learn more generalizable and transferable features for other tasks, which demonstrates the effectiveness of SSL in incremental learning. Experimental results on benchmark datasets show that our approach significantly outperforms non-exemplar based methods, and achieves comparable performance compared to exemplar based approaches.

关键词： Training Learning systems Deep learning Data privacy computer vision Computational modeling Prototypes

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Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers 32

Modulating Image Restoration with Continual Levels via Adapt...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Jingwen Dong, Chao Qiao, Yu Chinese Acad Sci Shenzhen Inst Adv Technol ShenZhen Key Lab Comp Vis & Pattern Recognit SIAT SenseTime Joint Lab Shenzhen Guangdong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781728132938

In image restoration tasks, like denoising and super-resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of little additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.

关键词： Deep Learning Low-level vision

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Causal Hidden Markov Model for Time Series Disease Forecasting

Causal Hidden Markov Model for Time Series Disease Forecasti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Jing Wu, Botong Sun, Xinwei Wang, Yizhou Peking Univ Dept Comp Sci Beijing Peoples R China Peking Univ Adv Inst Info Tech Beijing Peoples R China Peking Univ Ctr Frontiers Comp Studies Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China Deepwise AI Lab Beijing Peoples R China

ISBN: (纸本)9781665445092

We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part;b) the disease (non-clinical)-related part;c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Further, the ablation study empirically shows the effectiveness of each component in our method. And the visualization shows the accurate identification of lesion regions from others. (1)

关键词： Correlation Time series analysis Hidden Markov models Medical treatment Predictive models Linear programming pattern recognition

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Not just Compete, but Collaborate: Local Image-to-Image Translation via Cooperative Mask Prediction

Not just Compete, but Collaborate: Local Image-to-Image Tran...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kim, Daejin Khan, Mohammad Azam Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea Dhaka Power Distribut Co Ltd Dhaka Bangladesh

ISBN: (纸本)9781665445092

Facial attribute editing aims to manipulate the image with the desired attribute while preserving the other details. Recently, generative adversarial networks along with the encoder-decoder architecture have been utilized for this task owing to their ability to create realistic images. However, the existing methods for the unpaired dataset cannot still preserve the attribute-irrelevant regions properly due to the absence of the ground truth image. This work proposes a novel, intuitive loss function called the CAM-consistency loss, which improves the consistency of an input image in image translation. While the existing cycle-consistency loss ensures that the image can be translated back, our approach makes the model further preserve the attribute-irrelevant regions even in a single translation to another domain by using the Grad-CAM output computed from the discriminator. Our CAM-consistency loss directly optimizes such a Grad-CAM output from the discriminator during training, in order to properly capture which local regions the generator should change while keeping the other regions unchanged. In this manner, our approach allows the generator and the discriminator to collaborate with each other to improve the image translation quality. In our experiments, we validate the effectiveness and versatility of our proposed CAM-consistency loss by applying it to several representative models for facial image editing, such as StarGAN, AttGAN, and STGAN.

关键词： Training computer vision Computational modeling computer architecture Predictive models Generative adversarial networks Generators

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Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds

Voxel Set Transformer: A Set-to-Set Approach to 3D Object De...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： He, Chenhang Li, Ruihuang Li, Shuai Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Transformer has demonstrated promising performance in many 2D vision tasks. However, it is cumbersome to compute the self-attention on large-scale point cloud data because point cloud is a long sequence and unevenly distributed in 3D space. To solve this issue, existing methods usually compute self-attention locally by grouping the points into clusters of the same size, or perform convolutional self-attention on a discretized representation. However, the former results in stochastic point dropout, while the latter typically has narrow attention fields. In this paper, we propose a novel voxel-based architecture, namely Voxel Set Transformer (VoxSeT), to detect 3D objects from point clouds by means of set-to-set translation. VoxSeT is built upon a voxel-based set attention (VSA) module, which reduces the self-attention in each voxel by two cross-attentions and models features in a hidden space induced by a group of latent codes. With the VSA module, VoxSeT can manage voxelized point clusters with arbitrary size in a wide range, and process them in parallel with linear complexity. The proposed VoxSeT integrates the high performance of transformer with the efficiency of voxel-based model, which can be used as a good alternative to the convolutional and point-based backbones. VoxSeT reports competitive results on the KITH and Waymo detection benchmarks. The source codes can be found at https://***/skyhehe123/VoxSet.

关键词： Point cloud compression Convolutional codes computer vision Three-dimensional displays Stochastic processes Distributed databases Object detection

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