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Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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發(fā)表于 2025-3-21 17:28:44 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision – ECCV 2022
副標題17th European Confer
編輯Shai Avidan,Gabriel Brostow,Tal Hassner
視頻videohttp://file.papertrans.cn/235/234250/234250.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app
描述.The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?.The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..
出版日期Conference proceedings 2022
關鍵詞Computer Science; Informatics; Conference Proceedings; Research; Applications
版次1
doihttps://doi.org/10.1007/978-3-031-19797-0
isbn_softcover978-3-031-19796-3
isbn_ebook978-3-031-19797-0Series 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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沙發(fā)
發(fā)表于 2025-3-21 20:38:11 | 只看該作者
,OSFormer: One-Stage Camouflaged Instance Segmentation with?Transformers,esign a . (LST) to obtain the location label and instance-aware parameters by introducing the location-guided queries and the blend-convolution feed-forward network. Second, we develop a . (CFF) to merge diverse context information from the LST encoder and CNN backbone. Coupling these two components
板凳
發(fā)表于 2025-3-22 01:04:33 | 只看該作者
Highly Accurate Dichotomous Image Segmentation,ages. To this end, we collected the first large-scale DIS dataset, called ., which contains 5,470 high-resolution (., 2K, 4K or larger) images covering ., ., or . in various backgrounds. DIS is annotated with extremely fine-grained labels. Besides, we introduce a simple intermediate supervision base
地板
發(fā)表于 2025-3-22 06:36:33 | 只看該作者
,Boosting Supervised Dehazing Methods via?Bi-level Patch Reweighting,rvised dehazing methods, in which all training patches are accounted for equally in the loss design. These supervised methods may fail in making promising recoveries on some regions contaminated by heavy hazes. Therefore, for a more reasonable dehazing losses design, the varying importance of differ
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發(fā)表于 2025-3-22 11:12:53 | 只看該作者
,Flow-Guided Transformer for?Video Inpainting,in transformer for high fidelity video inpainting. More specially, we design a novel flow completion network to complete the corrupted flows by exploiting the relevant flow features in a local temporal window. With the completed flows, we propagate the content across video frames, and adopt the flow
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發(fā)表于 2025-3-22 14:12:00 | 只看該作者
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發(fā)表于 2025-3-22 19:59:30 | 只看該作者
,Perception-Distortion Balanced ADMM Optimization for?Single-Image Super-Resolution,rmance in one aspect due to the perception-distortion trade-off, and works that successfully balance the trade-off rely on fusing results from separately trained models with ad-hoc post-processing. In this paper, we propose a novel super-resolution model with a low-frequency constraint (LFc-SR), whi
8#
發(fā)表于 2025-3-22 22:37:02 | 只看該作者
,VQFR: Blind Face Restoration with?Vector-Quantized Dictionary and?Parallel Decoder,d facial details faithful to inputs remains a challenging problem. Motivated by the classical dictionary-based methods and the recent vector quantization (VQ) technique, we propose a VQ-based face restoration method – VQFR. VQFR takes advantage of high-quality low-level feature banks extracted from
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發(fā)表于 2025-3-23 02:57:53 | 只看該作者
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發(fā)表于 2025-3-23 05:55:25 | 只看該作者
,Learning Spatio-Temporal Downsampling for?Effective Video Upscaling,uch as moiré patterns in space and the wagon-wheel effect in time. Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts. In this paper, we aim to solve the space-tim
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