| Description |
1 online resource (xxvi, 783 pages) : illustrations (some color) |
| Series |
Lecture notes in computer science, 1611-3349 ; 13806 |
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Lecture notes in computer science ; 13806. 1611-3349
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| Contents |
Intro -- Foreword -- Preface -- Organization -- Contents - Part VI -- W22 -- Competition on Affective Behavior Analysis In-the-Wild -- W22 -- Competition on Affective Behavior Analysis In-the-Wild -- Geometric Pose Affordance: Monocular 3D Human Pose Estimation with Scene Constraints -- 1 Introduction -- 2 Related Work -- 3 Geometric Pose Affordance Dataset (GPA) -- 4 Geometry-Aware Pose Estimation -- 4.1 Pose Estimation Baseline Model -- 4.2 Geometric Consistency Loss and Encoding -- 4.3 Overall Training -- 5 Experiments -- 5.1 Baselines -- 5.2 Effectiveness of Geometric Affordance |
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6 Discussion and Conclusion -- References -- Affective Behaviour Analysis Using Pretrained Model with Facial Prior -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Formulation -- 3.2 EMMA -- 3.3 Masked CoTEX -- 4 Experiment -- 4.1 Benchmarks and Evaluation Metrics -- 4.2 Training Details -- 4.3 Recognition Results -- 4.4 Ablation Study -- 5 Conclusions -- References -- Facial Affect Recognition Using Semi-supervised Learning with Adaptive Threshold -- 1 Introduction -- 2 Method -- 2.1 MFAR: Multi-task Facial Affect Recognition |
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2.2 SS-MFAR: Semi-supervised Multi-task Facial Affect Recognition -- 2.3 Problem Formulation -- 2.4 Adaptive Threshold -- 2.5 Supervision Loss -- 2.6 Unsupervised and Consistency Loss -- 2.7 Overall Loss -- 3 Dataset and Implementation Details -- 3.1 Dataset -- 3.2 Implementation Details -- 3.3 Evaluation Metrics -- 4 Results and Discussion -- 4.1 Performance on Validation Set -- 4.2 Ablation Studies -- 4.3 Performance on Test Set -- 5 Conclusions -- References -- MT-EmotiEffNet for Multi-task Human Affective Behavior Analysis and Learning from Synthetic Data -- 1 Introduction |
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2 Proposed Approach -- 2.1 Multi-task Learning Challenge -- 2.2 Learning from Synthetic Data Challenge -- 3 Experimental Study -- 3.1 FER for Static Images -- 3.2 Multi-task-Learning Challenge -- 3.3 Learning from Synthetic Data Challenge -- 4 Conclusions -- References -- Ensemble of Multi-task Learning Networks for Facial Expression Recognition In-the-Wild with Learning from Synthetic Data -- 1 Introduction -- 2 Method -- 2.1 Data Pre-processing -- 2.2 Model Architecture -- 3 Experiments and Results -- 3.1 Training Setup -- 3.2 Performance Evaluation -- 4 Conclusion -- References |
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PERI: Part Aware Emotion Recognition in the Wild -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 MediaPipe Holistic Model -- 3.2 The Emotic Model -- 3.3 Part Aware Spatial Image -- 3.4 Context Infusion Blocks -- 4 Experiments -- 4.1 Experiment Setup -- 4.2 Quantitative Results -- 4.3 Qualitative Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Facial Expression Recognition with Mid-level Representation Enhancement and Graph Embedded Uncertainty Suppressing -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Mid-level Representation Enhancement |
| Summary |
The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online. The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge |
| Notes |
Includes author index |
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Online resource; title from PDF title page (SpringerLink, viewed February 20, 2023) |
| Subject |
Computer vision -- Congresses
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Pattern recognition systems -- Congresses
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Computer vision
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Pattern recognition systems
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| Genre/Form |
proceedings (reports)
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Conference papers and proceedings
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Conference papers and proceedings.
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Actes de congrès.
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| Form |
Electronic book
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| Author |
Karlinsky, Leonid, editor
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Michaeli, Tomer, editor.
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Nishino, Ko., editor.
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| ISBN |
9783031250750 |
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3031250753 |
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