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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

引用

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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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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Saliency-Guided Image Translation

Saliency-Guided Image Translation

引用

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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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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Open Domain Generalization with Domain-Augmented Meta-Learning

Open Domain Generalization with Domain-Augmented Meta-Learni...

引用

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

作者： Shu, Yang Cao, Zhangjie Wang, Chenyu Wang, Jianmin Long, Mingsheng Tsinghua Univ Sch Software BNRist Beijing Peoples R China

ISBN: (纸本)9781665445092

Leveraging datasets available to learn a model with high generalization ability to unseen domains is important for computer vision, especially when the unseen domain's annotated data are unavailable. We study a novel and practical problem of Open Domain Generalization (OpenDG), which learns from different source domains to achieve high performance on an unknown target domain, where the distributions and label sets of each individual source domain and the target domain can be different. The problem can be generally applied to diverse source domains and widely applicable to real-world applications. We propose a Domain-Augmented Meta-Learning framework to learn open-domain generalizable representations. We augment domains on both featurelevel by a new Dirichlet mixup and label-level by distilled soft-labeling, which complements each domain with missing classes and other domain knowledge. We conduct metalearning over domains by designing new meta-learning tasks and losses to preserve domain unique knowledge and generalize knowledge across domains simultaneously. Experiment results on various multi-domain datasets demonstrate that the proposed Domain-Augmented Meta-Learning (DAML) outperforms prior methods for unseen domain recognition.

关键词： computer vision Computational modeling Data models pattern recognition Microstrip Task analysis

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APES: Audiovisual Person Search in Untrimmed Video

APES: Audiovisual Person Search in Untrimmed Video

引用

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

作者： Alcazar, Juan Leon Heilbron, Fabian Caba Mai, Long Perazzi, Federico Lee, Joon-Young Arbelaez, Pablo Ghanem, Bernard Univ Los Andes Bogota Colombia Adobe Res San Jose CA USA King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (纸本)9781665448994

Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous efforts in the person re-identification and retrieval domains, few works have developed audiovisual search strategies. In this paper, we present the Audiovisual Person Search dataset (APES), a new dataset composed of untrimmed videos whose audio (voices) and visual (faces) streams are densely annotated. APES contains over 1.9K identities labeled along 36 hours of video, making it the largest dataset available for untrimmed audiovisual person search. A key property of APES is that it includes dense temporal annotations that link faces to speech segments of the same identity. To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval. Our study shows that modeling audiovisual cues benefits the recognition of people's identities.

关键词： Visualization computer vision Annotations conferences Streaming media Benchmark testing Search problems

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DANet: Dynamic Attention to Spoof patterns for Face Anti-Spoofing 26

DANet: Dynamic Attention to Spoof Patterns for Face Anti-Spo...

引用

26th International conference on pattern recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Sun, Chun-Yu Chen, Song-Lu Li, Xin-Jie Chen, Feng Yin, Xu-Cheng Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Univ Sci & Technol Beijing USTB EEasyTech Joint Lab Artificial Intelligence Beijing Peoples R China EEasy Technol Co Ltd Zhuhai Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Face anti-spoofing is a vital part to protect the security of face recognition systems. Many existing face anti-spoofing methods rely on convolutional neural networks (CNNs) and achieve competitive performance. However, due to the power of CNNs, these methods will extract information that is irrelevant to spoof patterns, such as acquisition equipment and environmental characteristics, which makes the network vulnerable to changes of the illumination or camera. In this work, we propose a plug-and-play module called DyAttention, which can improve the robustness against environmental changes. Moreover, we build a network named DANet with DyAttention, which can accurately capture the spoof patterns from coarse to fine. DANet can dynamically capture the texture differences between live and spoof samples in the facial area. Specifically, we use the spatial attention mechanism to generate a mask of the facial area. Then, we extract the intrinsic texture patterns and piecewise enhance them via dynamic activation for clean representation, where the texture patterns are not affected by the environmental and domain factors. Through experiments on three benchmark datasets, our DANet achieves state-of-the-art intra-dataset accuracy on CASIA-MFSD, Replay-Attack, and OULU-NPU. Meanwhile, DANet can enhance the cross-dataset performance between CASIA-MFSD and Replay-Attack, improving the average HTER by 1.3%.

关键词： computer vision Face recognition Lighting Feature extraction Cameras Robustness Security

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

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

引用

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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