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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video
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 networks; computer vision; deep learning; education; Human-Computer Int
版次1
doihttps://doi.org/10.1007/978-3-031-25066-8
isbn_softcover978-3-031-25065-1
isbn_ebook978-3-031-25066-8Series 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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Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challen. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized
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AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Resultse super-resolution of compressed image, and Track 2 targets the super-resolution of compressed video. In Track 1, we use the popular dataset DIV2K as the training, validation and test sets. In Track 2, we propose the LDV 3.0 dataset, which contains 365 videos, including the LDV 2.0 dataset (335 vide
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Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentationsed on U-shaped architecture and skip-connections have been widely applied in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global semantic information interaction well due to the locality of convolution operation. In this paper, we propose Sw
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Self-attention Capsule Network for Tissue Classification in Case of Challenging Medical Image Statisclassification. These challenges are - significant data heterogeneity with statistics variability across imaging domains, insufficient spatial context and local fine-grained details, and limited training data. Moreover, our proposed method solves limitations of the baseline Capsule Networks (CapsNet
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