001454719 000__ 07525cam\a2200577\i\4500
001454719 001__ 1454719
001454719 003__ OCoLC
001454719 005__ 20230314003225.0
001454719 006__ m\\\\\o\\d\\\\\\\\
001454719 007__ cr\cn\nnnunnun
001454719 008__ 230220s2023\\\\sz\a\\\\o\\\\\101\0\eng\d
001454719 020__ $$a9783031250750$$q(electronic bk.)
001454719 020__ $$a3031250753$$q(electronic bk.)
001454719 020__ $$z9783031250743
001454719 0247_ $$a10.1007/978-3-031-25075-0$$2doi
001454719 035__ $$aSP(OCoLC)1370619186
001454719 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP
001454719 049__ $$aISEA
001454719 050_4 $$aTA1634
001454719 08204 $$a006.3/7$$223/eng/20230220
001454719 1112_ $$aEuropean Conference on Computer Vision$$n(17th :$$d2022 :$$cTel Aviv, Israel)
001454719 24510 $$aComputer vision -- ECCV 2022 Workshops :$$bTel Aviv, Israel, October 23-27, 2022, Proceedings.$$nPart VI /$$cLeonid Karlinsky, Tomer Michaeli, Ko Nishino (eds.).
001454719 2463_ $$aECCV 2022
001454719 264_1 $$aCham :$$bSpringer,$$c2023.
001454719 300__ $$a1 online resource (xxvi, 783 pages) :$$billustrations (some color).
001454719 336__ $$atext$$btxt$$2rdacontent
001454719 337__ $$acomputer$$bc$$2rdamedia
001454719 338__ $$aonline resource$$bcr$$2rdacarrier
001454719 4901_ $$aLecture notes in computer science,$$x1611-3349 ;$$v13806
001454719 500__ $$aIncludes author index.
001454719 5050_ $$aIntro -- 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
001454719 5058_ $$a6 Discussion and Conclusion -- References -- Affective Behaviour Analysis Using Pretrained Model with Facial Prior -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Formulation -- 3.2 EMMA -- 3.3 Masked CoTEX -- 4 Experiment -- 4.1 Benchmarks and Evaluation Metrics -- 4.2 Training Details -- 4.3 Recognition Results -- 4.4 Ablation Study -- 5 Conclusions -- References -- Facial Affect Recognition Using Semi-supervised Learning with Adaptive Threshold -- 1 Introduction -- 2 Method -- 2.1 MFAR: Multi-task Facial Affect Recognition
001454719 5058_ $$a2.2 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
001454719 5058_ $$a2 Proposed Approach -- 2.1 Multi-task Learning Challenge -- 2.2 Learning from Synthetic Data Challenge -- 3 Experimental Study -- 3.1 FER for Static Images -- 3.2 Multi-task-Learning Challenge -- 3.3 Learning from Synthetic Data Challenge -- 4 Conclusions -- References -- Ensemble of Multi-task Learning Networks for Facial Expression Recognition In-the-Wild with Learning from Synthetic Data -- 1 Introduction -- 2 Method -- 2.1 Data Pre-processing -- 2.2 Model Architecture -- 3 Experiments and Results -- 3.1 Training Setup -- 3.2 Performance Evaluation -- 4 Conclusion -- References
001454719 5058_ $$aPERI: 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
001454719 506__ $$aAccess limited to authorized users.
001454719 520__ $$aThe 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.
001454719 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 20, 2023).
001454719 650_0 $$aComputer vision$$vCongresses.
001454719 650_0 $$aPattern recognition systems$$vCongresses.
001454719 655_0 $$aElectronic books.
001454719 7001_ $$aKarlinsky, Leonid,$$eeditor.
001454719 7001_ $$aMichaeli, Tomer,$$eeditor.$$0(orcid)0000-0003-0525-8054$$1https://orcid.org/0000-0003-0525-8054
001454719 7001_ $$aNishino, Ko.,$$eeditor.$$1https://orcid.org/0000-0002-3534-3447
001454719 830_0 $$aLecture notes in computer science ;$$v13806.$$x1611-3349
001454719 852__ $$bebk
001454719 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-25075-0$$zOnline Access$$91397441.1
001454719 909CO $$ooai:library.usi.edu:1454719$$pGLOBAL_SET
001454719 980__ $$aBIB
001454719 980__ $$aEBOOK
001454719 982__ $$aEbook
001454719 983__ $$aOnline
001454719 994__ $$a92$$bISE