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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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發(fā)表于 2025-3-21 16:03:31 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision – ECCV 2022 Workshops
副標題Tel Aviv, Israel, Oc
編輯Leonid Karlinsky,Tomer Michaeli,Ko Nishino
視頻videohttp://file.papertrans.cn/235/234282/234282.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面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
關(guān)鍵詞artificial intelligence; computer networks; computer systems; computer vision; deep learning; education; i
版次1
doihttps://doi.org/10.1007/978-3-031-25082-8
isbn_softcover978-3-031-25081-1
isbn_ebook978-3-031-25082-8Series 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
The information of publication is updating

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234282.jpg
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HLA and ABO antigens in keratoconus patientsesources and therefore cannot be deployed on edge devices. It is of great interest to build resource-efficient general purpose networks due to their usefulness in several application areas. In this work, we strive to effectively combine the strengths of both CNN and Transformer models and propose a
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HLA and ABO antigens in keratoconus patientsference. This paper offers a comprehensive introduction and guide to CINs and their implementation, as well as best-practices and code examples for composing basic modules into complex neural network architectures that perform online inference with an order of magnitude less floating-point operation
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HLA and ABO antigens in keratoconus patientshis is that self-attention scales quadratically with the number of tokens, which in turn, scales quadratically with the image size. On larger images (e.g., 1080p), over 60% of the total computation in the network is spent solely on creating and applying attention matrices. We take a step toward solv
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發(fā)表于 2025-3-22 18:14:54 | 只看該作者
Studies in Computational Intelligencefor reduced memory usage and computation, while preserving the performance of the original model. However, extreme quantization (1-bit weight/1-bit activations) of compactly-designed backbone architectures (e.g., MobileNets) often used for edge-device deployments results in severe performance degene
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Studies in Computational Intelligenceover time. Dynamic network architectures are one of the existing techniques developed to handle the varying computational load in real-time deployments. Here we introduce LeAF (Legacy Augmentation for Flexible inference), a novel paradigm to augment the key-phases of a pre-trained DNN with alternati
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