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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22907 条记录，以下是4571-4580 订阅

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Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking

Discriminative Appearance Modeling with Multi-track Pooling ...

引用

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

作者： Kim, Chanho Li Fuxin Alotaibi, Mazen Rehg, James M. Georgia Inst Technol Atlanta GA 30332 USA Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781665445092

In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches between tracks and detections, and is updated based on the matching. Many approaches model each target in isolation and lack the ability to use all the targets in the scene to jointly update the memory. This can be problematic when there are similarly looking objects in the scene. In this paper, we solve the problem of simultaneously considering all tracks during memory updating, with only a small spatial overhead, via a novel multi-track pooling module. We additionally propose a training strategy adapted to multi-track pooling which generates hard tracking episodes online. We show that the combination of these innovations results in a strong discriminative appearance model under the bilinear LSTM tracking framework, enabling the use of greedy data association to achieve online tracking performance. Our experiments demonstrate real-time, state-of-the-art online tracking performance on public multi-object tracking (MOT) datasets.

关键词： Training Technological innovation computer vision Target tracking Codes computer architecture Real-time systems

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AidUI: Toward Automated recognition of Dark patterns in User Interfaces 23

AidUI: Toward Automated Recognition of Dark Patterns in User...

引用

45th ieee/ACM International conference on Software Engineering (ICSE)

作者： Mansur, S. M. Hasan Salma, Sabiha Awofisayo, Damilola Moran, Kevin George Mason Univ Dept Comp Sci Fairfax VA 22030 USA Duke Univ Dept Comp Sci Durham NC 27706 USA George Mason Univ Aspiring Scientist Summer Internship Program ASSI Fairfax VA 22030 USA

ISBN: (纸本)9781665457019

Past studies have illustrated the prevalence of UI dark patterns, or user interfaces that can lead end-users toward (unknowingly) taking actions that they may not have intended. Such deceptive UI designs can be either intentional (to benefit an online service) or unintentional (through complicit design practices) and can result in adverse effects on end users, such as oversharing personal information or financial loss. While significant research progress has been made toward the development of dark pattern taxonomies across different software domains, developers and users currently lack guidance to help recognize, avoid, and navigate these often subtle design motifs. However, automated recognition of dark patterns is a challenging task, as the instantiation of a single type of pattern can take many forms, leading to significant variability. In this paper, we take the first step toward understanding the extent to which common UI dark patterns can be automatically recognized in modern software applications. To do this, we introduce AIDUI, a novel automated approach that uses computer vision and natural language processing techniques to recognize a set of visual and textual cues in application screenshots that signify the presence of ten unique UI dark patterns, allowing for their detection, classification, and localization. To evaluate our approach, we have constructed CONTEXTDP, the current largest dataset of fully-localized UI dark patterns that spans 175 mobile and 83 web UI screenshots containing 301 dark pattern instances. The results of our evaluation illustrate that AIDUI achieves an overall precision of 0.66, recall of 0.67, F1-score of 0.65 in detecting dark pattern instances, reports few false positives, and is able to localize detected patterns with an IoU score of 0.84. Furthermore, a significant subset of our studied dark patterns can be detected quite reliably (F1 score of over 0.82), and future research directions may allow for improved detection of add

关键词： Dark pattern UI Analysis UI Design

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Multi-Scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units recognition

Multi-Scale Dynamic and Hierarchical Relationship Modeling f...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zihan Wang Siyang Song Cheng Luo Songhe Deng Weicheng Xie Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Universiry of Leicester Monash University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hi-erarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatiotemporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and mag-nitude in different AUs' activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR.

关键词： Gold Adaptation models computer vision Graphical models Codes Adaptive systems Computational modeling

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FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds

FESTA: Flow Estimation via Spatial-Temporal Attention for Sc...

引用

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

作者： Wang, Haiyan Pang, Jiahao Lodhi, Muhammad A. Tian, Yingli Tian, Dong InterDigital Wilmington DE 19809 USA CUNY City Coll New York NY 10031 USA

ISBN: (纸本)9781665445092

Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is estimated from dense/regular RGB video frames. With the development of depth-sensing technologies, precise 3D measurements are available via point clouds which have sparked new research in 3D scene flow. Nevertheless, it remains challenging to extract scene flow from point clouds due to the sparsity and irregularity in typical point cloud sampling patterns. One major issue related to irregular sampling is identified as the randomness during point set abstraction/feature extraction-an elementary process in many flow estimation scenarios. A novel Spatial Abstraction with Attention (SA(2)) layer is accordingly proposed to alleviate the unstable abstraction problem. Moreover, a Temporal Abstraction with Attention (TA(2)) layer is proposed to rectify attention in temporal domain, leading to benefits with motions scaled in a larger range. Extensive analysis and experiments verified the motivation and significant performance gains of our method, dubbed as Flow Estimation via Spatial-Temporal Attention (FESTA), when compared to several state-of-the-art benchmarks of scene flow estimation.

关键词： computer vision Three-dimensional displays Image recognition Image coding Navigation Computational modeling Estimation

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Medium Scale Benchmark for Cricket Excited Actions Understanding

Medium Scale Benchmark for Cricket Excited Actions Understan...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Altaf Hussain Noman Khan Muhammad Munsif Min Je Kim Sung Wook Baik Sejong University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Sports Action recognition (SAR) domain is of significant importance in research, with diverse applications, ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets, the direct application of these datasets to specific sports domains, such as cricket, poses challenges. Existing datasets predominantly center around daily life actions, lacking the necessary granularity for in-depth sports analyses. Current Cricket Action Analysis (CAA) datasets have limitations, including their small scale, modality constraints, and their narrow focus on specific aspects, such as cricket batting. Recognizing the need for a more comprehensive benchmark, this article introduces the Cricket Excited Actions (CEA) dataset. Developed in collaboration with professional cricket players, the CEA dataset encompasses challenging multi-person actions within realistic cricket scenarios. The selected activity classes, such as Clean Bowled, Six, Four, and Catches, adhere to official standards and represent pivotal moments in cricket matches. Through precise annotation and empirical studies, utilizing state-of-the-art action recognition model architectures, this study provides a valuable resource for further research and makes significant contributions by offering support essential to advancing CAA within the cricket sports community. The data and code are available at https://***/Altaf-hucn/Cricket-Excited-Actions-Benchmark.

关键词： Computational modeling Refining Collaboration computer architecture Benchmark testing Real-time systems pattern recognition

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Weakly-Supervised Physically Unconstrained Gaze Estimation

Weakly-Supervised Physically Unconstrained Gaze Estimation

引用

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

作者： Kothari, Rakshit De Mello, Shalini Iqbal, Umar Byeon, Wonmin Park, Seonwook Kautz, Jan NVIDIA Santa Clara CA 95051 USA Rochester Inst Technol Rochester NY 14623 USA Lunit Inc Seoul South Korea

ISBN: (纸本)9781665445092

A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of human interactions in unconstrained environments are abundantly available and can be much more easily annotated with frame-level activity labels. In this work, we tackle the previously unexplored problem of weakly-supervised gaze estimation from videos of human interactions. We leverage the insight that strong gaze-related geometric constraints exist when people perform the activity of "looking at each other" (LAEO). To acquire viable 3D gaze supervision from LAEO labels, we propose a training algorithm along with several novel loss functions especially designed for the task. With weak supervision from two large scale CMU-Panoptic and AVA-LAEO activity datasets, we show significant improvements in (a) the accuracy of semisupervised gaze estimation and (b) cross-domain generalization on the state-of-the-art physically unconstrained in-the-wild Gaze360 gaze estimation benchmark.

关键词： Training Geometry Three-dimensional displays Annotations Estimation Training data pattern recognition

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Separating Skills and Concepts for Novel Visual Question Answering

Separating Skills and Concepts for Novel Visual Question Ans...

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

作者： Whitehead, Spencer Wu, Hui Ji, Heng Feris, Rogerio Saenko, Kate UIUC Urbana IL 61801 USA IBM Res MIT IBM Watson AI Lab Yorktown Hts NY USA Boston Univ Boston MA 02215 USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9781665445092

Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question, such as objects and people. VQA methods should be able to compose skills and concepts in novel ways, regardless of whether the specific composition has been seen in training, yet we demonstrate that existing models have much to improve upon towards handling new compositions. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. We enforce these properties with a novel contrastive learning procedure that does not rely on external annotations and can be learned from unlabeled image-question pairs. Experiments demonstrate the effectiveness of our approach for improving compositional and grounding performance.(1)

关键词： Training Visualization computer vision Grounding Annotations Knowledge discovery Encoding

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Reconstructing 3D Human Pose by Watching Humans in the Mirror

Reconstructing 3D Human Pose by Watching Humans in the Mirro...

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

作者： Fang, Qi Shuai, Qing Dong, Junting Bao, Hujun Zhou, Xiaowei Zhejiang Univ State Key Lab CAD&CG Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

In this paper, we introduce the new task of reconstructing 3D human pose from a single image in which we can see the person and the person's image through a mirror. Compared to general scenarios of 3D pose estimation from a single view, the mirror reflection provides an additional view for resolving the depth ambiguity. We develop an optimization-based approach that exploits mirror symmetry constraints for accurate 3D pose reconstruction. We also provide a method to estimate the surface normal of the mirror from vanishing points in the single image. To validate the proposed approach, we collect a large-scale dataset named Mirrored-Human, which covers a large variety of human subjects, poses and backgrounds. The experiments demonstrate that, when trained on Mirrored-Human with our reconstructed 3D poses as pseudo ground-truth, the accuracy and generalizability of existing single-view 3D pose estimators can be largely improved. The code and dataset are available at https://***/Mirrored-Human/.

关键词： Training Surface reconstruction Three-dimensional displays Shape Pose estimation Reflection pattern recognition

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Neural Scene Graphs for Dynamic Scenes

Neural Scene Graphs for Dynamic Scenes

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

作者： Ost, Julian Mannan, Fahim Thuerey, Nils Knodt, Julian Heide, Felix Algolux Montreal PQ Canada Tech Univ Munich Munich Germany Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images. However, existing methods are restricted to learning efficient representations of static scenes that encode all scene objects into a single neural network, and they lack the ability to represent dynamic scenes and decompose scenes into individual objects. In this work, we present the first neural rendering method that represents multi-object dynamic scenes as scene graphs. We propose a learned scene graph representation, which encodes object transformations and radiance, allowing us to efficiently render novel arrangements and views of the scene. To this end, we learn implicitly encoded scenes, combined with a jointly learned latent representation to describe similar objects with a single implicit function. We assess the proposed method on synthetic and real automotive data, validating that our approach learns dynamic scenes - only by observing a video of this scene - and allows for rendering novel photo-realistic views of novel scene compositions with unseen sets of objects at unseen poses.

关键词： Training computer vision Extrapolation Image color analysis Neural networks Training data Object detection

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Graph-based High-Order Relation Discovery for Fine-grained recognition

Graph-based High-Order Relation Discovery for Fine-grained R...

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

作者： Zhao, Yifan Yan, Ke Huang, Feiyue Li, Jia Beihang Univ State Key Lab Virtual Real Technol & Syst SCSE Beijing Peoples R China Tencent Youtu Lab Shanghai Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Fine-grained object recognition aims to learn effective features that can identify the subtle differences between visually similar objects. Most of the existing works tend to amplify discriminative part regions with attention mechanisms. Besides its unstable performance under complex backgrounds, the intrinsic interrelationship between different semantic features is less explored. Toward this end, we propose an effective graph-based relation discovery approach to build a contextual understanding of high-order relationships. In our approach, a high-dimensional feature bank is first formed and jointly regularized with semantic- and positional-aware high-order constraints, endowing rich attributes to feature representations. Second, to overcome the high-dimension curse, we propose a graph-based semantic grouping strategy to embed this high-order tensor bank into a low-dimensional space. Meanwhile, a group-wise learning strategy is proposed to regularize the features focusing on the cluster embedding center. With the collaborative learning of three modules, our module is able to grasp the stronger contextual details of fine-grained objects. Experimental evidence demonstrates our approach achieves new state-of-the-art on 4 widely-used fine-grained object recognition benchmarks.

关键词： Training Manifolds computer vision Tensors Semantics Focusing Collaborative work

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