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Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video

Beyond Static Features for Temporally Consistent 3D Human Po...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Choi, Hongsuk Moon, Gyeongsik Chang, Ju Yong Lee, Kyoung Mu Seoul Natl Univ ECE Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea Kwangwoon Univ ECE Seoul South Korea

ISBN: (纸本)9781665445092

Despite the recent success of single image-based 3D human pose and shape estimation methods, recovering temporally consistent and smooth 3D human motion from a video is still challenging. Several video-based methods have been proposed;however, they fail to resolve the single image-based methods' temporal inconsistency issue due to a strong dependency on a static feature of the current frame. In this regard, we present a temporally consistent mesh recovery system (TCMR). It effectively focuses on the past and future frames' temporal information without being dominated by the current static feature. Our TCMR significantly outperforms previous video-based methods in temporal consistency with better per-frame 3D pose and shape accuracy. We also release the codes.

关键词： computer vision Three-dimensional displays Image resolution Codes Shape Estimation pattern recognition

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A Hyperbolic-to-Hyperbolic Graph Convolutional Network

A Hyperbolic-to-Hyperbolic Graph Convolutional Network

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dai, Jindou Wu, Yuwei Gao, Zhi Jia, Yunde Beijing Inst Technol BIT Lab Intelligent Informat Technol Sch Comp Sci Beijing 100081 Peoples R China

ISBN: (纸本)9781665445092

Hyperbolic graph convolutional networks (GCNs) demonstrate powerful representation ability to model graphs with hierarchical structure. Existing hyperbolic GCNs resort to tangent spaces to realize graph convolution on hyperbolic manifolds, which is inferior because tangent space is only a local approximation of a manifold. In this paper, we propose a hyperbolic-to-hyperbolic graph convolutional network (H2H-GCN) that directly works on hyperbolic manifolds. Specifically, we developed a manifold-preserving graph convolution that consists of a hyperbolic feature transformation and a hyperbolic neighborhood aggregation. The hyperbolic feature transformation works as linear transformation on hyperbolic manifolds. It ensures the transformed node representations still lie on the hyperbolic manifold by imposing the orthogonal constraint on the transformation sub-matrix. The hyperbolic neighborhood aggregation updates each node representation via the Einstein midpoint. The H2H-GCN avoids the distortion caused by tangent space approximations and keeps the global hyperbolic structure. Extensive experiments show that the H2H-GCN achieves substantial improvements on the link prediction, node classification, and graph classification tasks.

关键词： Manifolds Geometry computer vision Convolution Distortion pattern recognition Task analysis

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Fully Convolutional Networks for Panoptic Segmentation

Fully Convolutional Networks for Panoptic Segmentation

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Yanwei Zhao, Hengshuang Qi, Xiaojuan Wang, Liwei Li, Zeming Sun, Jian Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Oxford Oxford England Univ Hong Kong Hong Kong Peoples R China MEGVII Technol Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9781665445092

In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foreground things and background stuff in a unified fully convolutional pipeline. In particular, Panoptic FCN encodes each object instance or stuff category into a specific kernel weight with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly. With this approach, instance-aware and semantically consistent prosperties for things and stuff can be respectively satisfied in a simple generate-kernel-then-segment workflow. Without extra boxes for localization or instance separation, the proposed approach outperforms previous box-based and -free models with high efficiency on COCO, Cityscapes, and Mapillary Vistas datasets with single scale input.

关键词： Convolutional codes Location awareness computer vision Semantics Pipelines Generators pattern recognition

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Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning

Iterative Shrinking for Referring Expression Grounding Using...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sun, Mingjie Xiao, Jimin Lim, Eng Gee Xian Jiaotong Liverpool Univ Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England

ISBN: (纸本)9781665445092

In this paper, we are tackling the proposal-free referring expression grounding task, aiming at localizing the target object according to a query sentence, without relying on off-the-shelf object proposals. Existing proposal-free methods employ a query-image matching branch to select the highest-score point in the image feature map as the target box center, with its width and height predicted by another branch. Such methods, however, fail to utilize the contextual relation between the target and reference objects, and lack interpretability on its reasoning procedure. To solve these problems, we propose an iterative shrinking mechanism to localize the target, where the shrinking direction is decided by a reinforcement learning agent, with all contents within the current image patch comprehensively considered. Besides, the sequential shrinking processes enable to demonstrate the reasoning about how to iteratively find the target. Experiments show that the proposed method boosts the accuracy by 4.32% against the previous state-of-the-art (SOTA) method on the RefCOCOg dataset, where query sentences are long and complex with many targets referred by other reference objects.

关键词： computer vision Art Grounding Reinforcement learning Cognition pattern recognition Proposals

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Variational Pedestrian Detection

Variational Pedestrian Detection

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Yuang He, Huanyu Li, Jianguo Li, Yuxi See, John Lin, Weiyao Shanghai Jiao Tong Univ Shanghai Peoples R China Ant Grp Hangzhou Peoples R China Heriot Watt Univ Putrajaya Malaysia

ISBN: (纸本)9781665445092

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the current IoU-based ground truth assignment procedure in classical object detection methods. In this paper, we develop a unique perspective of pedestrian detection as a variational inference problem. We formulate a novel and efficient algorithm for pedestrian detection by modeling the dense proposals as a latent variable while proposing a customized Auto-Encoding Variational Bayes (AEVB) algorithm. Through the optimization of our proposed algorithm, a classical detector can be fashioned into a variational pedestrian detector. Experiments conducted on CrowdHuman and CityPersons datasets show that the proposed algorithm serves as an efficient solution to handle the dense pedestrian detection problem for the case of single-stage detectors. Our method can also be flexibly applied to two-stage detectors, achieving notable performance enhancement.

关键词： computer vision Computational modeling Detectors Object detection Propulsion Inference algorithms pattern recognition

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Conditional Bures Metric for Domain Adaptation

Conditional Bures Metric for Domain Adaptation

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Luo, You-Wei Ren, Chuan-Xian Sun Yat Sen Univ Sch Math Guangzhou Guangdong Peoples R China Pazhou Lab Guangzhou Peoples R China

ISBN: (纸本)9781665445092

As a vital problem in classification-oriented transfer, unsupervised domain adaptation (UDA) has attracted widespread attention in recent years. Previous UDA methods assume the marginal distributions of different domains are shifted while ignoring the discriminant information in the label distributions. This leads to classification performance degeneration in real applications. In this work, we focus on the conditional distribution shift problem which is of great concern to current conditional invariant models. We aim to seek a kernel covariance embedding for conditional distribution which remains yet unexplored. Theoretically, we propose the Conditional Kernel Bures (CKB) metric for characterizing conditional distribution discrepancy, and derive an empirical estimation for the CKB metric without introducing the implicit kernel feature map. It provides an interpretable approach to understand the knowledge transfer mechanism. The established consistency theory of the empirical estimation provides a theoretical guarantee for convergence. A conditional distribution matching network is proposed to learn the conditional invariant and discriminative features for UDA. Extensive experiments and analysis show the superiority of our proposed model.

关键词： Measurement computer vision Analytical models Adaptation models Computational modeling Estimation pattern recognition

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Where and What? Examining Interpretable Disentangled Representations

Where and What? Examining Interpretable Disentangled Represe...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Xinqi Xu, Chang Tao, Dacheng Univ Sydney Sydney NSW Australia

ISBN: (纸本)9781665445092

Capturing interpretable variations has long been one of the goals in disentanglement learning. However, unlike the independence assumption, interpretability has rarely been exploited to encourage disentanglement in the unsupervised setting. In this paper, we examine the interpretability of disentangled representations by investigating two questions: where to be interpreted and what to be interpreted? A latent code is easily to be interpreted if it would consistently impact a certain subarea of the resulting generated image. We thus propose to learn a spatial mask to localize the effect of each individual latent dimension. On the other hand, interpretability usually comes from latent dimensions that capture simple and basic variations in data. We thus impose a perturbation on a certain dimension of the latent code, and expect to identify the perturbation along this dimension from the generated images so that the encoding of simple variations can be enforced. Additionally, we develop an unsupervised model selection method, which accumulates perceptual distance scores along axes in the latent space. On various datasets, our models can learn high-quality disentangled representations without supervision, showing the proposed modeling of interpretability is an effective proxy for achieving unsupervised disentanglement.

关键词： computer vision Codes Image coding Perturbation methods Computational modeling Encoding pattern recognition

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Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

Adversarial Laser Beam: Effective Physical-World Attack to D...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Duan, Ranjie Mao, Xiaofeng Qin, A. K. Chen, Yuefeng Ye, Shaokai He, Yuan Yang, Yun Swinburne Univ Technol Hawthorn Vic Australia Alibaba Grp Hangzhou Zhejiang Peoples R China Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781665445092

Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNNs are easily fooled. To this end, we propose a novel attack method called Adversarial Laser Beam (AdvLB), which enables manipulation of laser beam's physical parameters to perform adversarial attack. Experiments demonstrate the effectiveness of our proposed approach in both digital- and physical-settings. We further empirically analyze the evaluation results and reveal that the proposed laser beam attack may lead to some interesting prediction errors of the state-of-the-art DNNs. We envisage that the proposed AdvLB method enriches the current family of adversarial attacks and builds the foundation for future robustness studies for light.

关键词： Deep learning Laser theory computer vision Robustness pattern recognition Laser beams

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Saliency-Guided Image Translation

Saliency-Guided Image Translation

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jiang, Lai Xu, Mai Wang, Xiaofei Sigal, Leonid Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Univ British Columbia Dept Comp Sci Vancouver BC Canada

ISBN: (纸本)9781665445092

In this paper, we propose a novel task for saliency-guided image translation, with the goal of image-to-image translation conditioned on the user specified saliency map. To address this problem, we develop a novel Generative Adversarial Network (GAN)-based model, called SalG-GAN. Given the original image and target saliency map, SalG-GAN can generate a translated image that satisfies the target saliency map. In SalG-GAN, a disentangled representation framework is proposed to encourage the model to learn diverse translations for the same target saliency condition. A saliency-based attention module is introduced as a special attention mechanism for facilitating the developed structures of saliency-guided generator, saliency cue encoder and saliency-guided global and local discriminators. Furthermore, we build a synthetic dataset and a real-world dataset with labeled visual attention for training and evaluating our SalG-GAN. The experimental results over both datasets verify the effectiveness of our model for saliency-guided image translation.

关键词： Training Visualization computer vision Generative adversarial networks Generators pattern recognition Task analysis

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Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentation

Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Zixiang Zhang, Yang Foroosh, Hassan Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Waymo LLC Mountain View CA USA

ISBN: (纸本)9781665445092

Panoptic segmentation presents a new challenge in exploiting the merits of both detection and segmentation, with the aim of unifying instance segmentation and semantic segmentation in a single framework. However, an efficient solution for panoptic segmentation in the emerging domain of LiDAR point cloud is still an open research problem and is very much under-explored. In this paper, we present a fast and robust LiDAR point cloud panoptic segmentation framework, referred to as Panoptic-PolarNet. We learn both semantic segmentation and class-agnostic instance clustering in a single inference network using a polar Bird's Eye View (BEV) representation, enabling us to circumvent the issue of occlusion among instances in urban street scenes. To improve our network's learnability, we also propose an adapted instance augmentation technique and a novel adversarial point cloud pruning method. Our experiments show that Panoptic-PolarNet outperforms the baseline methods on SemanticKITTI and nuScenes datasets with an almost real-time inference speed. Panoptic-PolarNet achieved 54.1% PQ in the public SemanticKITTI panoptic segmentation leaderboard and leading performance for the validation set of nuScenes.

关键词： computer vision Laser radar Semantics Real-time systems pattern recognition Complexity theory

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