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Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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书目名称Computer Vision – ECCV 2022 Workshops
副标题Tel Aviv, Israel, Oc
编辑Leonid Karlinsky,Tomer Michaeli,Ko Nishino
视频videohttp://file.papertrans.cn/235/234284/234284.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit
描述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-LevelAutonomous 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
出版日期Conference proceedings 2023
关键词artificial intelligence; computer vision; education; face recognition; gesture recognition; Human-Compute
版次1
doihttps://doi.org/10.1007/978-3-031-25085-9
isbn_softcover978-3-031-25084-2
isbn_ebook978-3-031-25085-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Trans6D: Transformer-Based 6D Object Pose Estimation and Refinementdows, cross-attention, and token pooling operations, which is used to predict dense 2D-3D correspondence maps; (ii) a pure Transformer-based pose refinement module (Trans6D+) which refines the estimated poses iteratively. Extensive experiments show that the proposed approach achieves state-of-the-ar
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Learning to Estimate Multi-view Pose from Object Silhouettes cues for multi-view relationships in a data-driven way. We show that our network generalizes to unseen synthetic and real object instances under reasonable assumptions about the input pose distribution of the images, and that the estimates are suitable to initialize state-of-the-art 3D reconstructi
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Fuse and Attend: Generalized Embedding Learning for Art and Sketchesmains. During training, given a query image from a domain, we employ gated fusion and attention to generate a positive example, which carries a broad notion of the semantics of the query object category (from across multiple domains). By virtue of Contrastive Learning, we pull the embeddings of the
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Lothar Lammersen,Robert Schwagers. To tackle these limitations, we propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets (left, right, top, bottom) that are homogeneous, so that it can tell which direction is uncer
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