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Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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發(fā)表于 2025-3-21 17:38:25 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Computer Vision – ECCV 2024
副標(biāo)題18th European Confer
編輯Ale? Leonardis,Elisa Ricci,Gül Varol
視頻videohttp://file.papertrans.cn/243/242314/242314.mp4
叢書(shū)名稱Lecture Notes in Computer Science
圖書(shū)封面Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic
描述.The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They 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; motion estimation..
出版日期Conference proceedings 2025
關(guān)鍵詞artificial intelligence; computer networks; computer systems; computer vision; education; Human-Computer
版次1
doihttps://doi.org/10.1007/978-3-031-72646-0
isbn_softcover978-3-031-72645-3
isbn_ebook978-3-031-72646-0Series 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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,OGNI-DC: Robust Depth Completion with?Optimization-Guided Neural Iterations,el framework for depth completion. The?key to our method is “.ptimization-.uided .eural .terations” (OGNI). It consists of?a recurrent unit that refines a depth gradient field and?a differentiable depth integrator that integrates the depth gradients into a depth map. OGNI-DC exhibits strong generali
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Beta-Tuned Timestep Diffusion Model,cent studies indicate that treating?all distributions equally in diffusion model training is sub-optimal.?In this paper, we conduct an in-depth theoretical analysis of?the forward process of diffusion models. Our findings reveal that?the distribution variations are non-uniform throughout the diffusi
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,POA: Pre-training Once for?Models of?All Sizes,gies train a single?model of a certain size at one time. Nevertheless, various computation?or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes?to deploy. Thus, in this study, we propose a novel tri-branch self-supervised train
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,Taming Latent Diffusion Model for?Neural Radiance Field Inpainting, editing a reconstructed NeRF with diffusion prior, they remain struggling to synthesize reasonable geometry in completely uncovered regions. One major reason is the high diversity of synthetic contents from the diffusion model, which hinders the radiance field from converging to a crisp and determi
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