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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 19:38:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱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/243/242345/242345.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面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/10.1007/978-3-031-73226-3
isbn_softcover978-3-031-73225-6
isbn_ebook978-3-031-73226-3Series 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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,Self-supervised Visual Learning from?Interactions with?Objects,od consistently outperforms previous methods on downstream category recognition. In our analysis, we find that the observed improvement is associated with a better viewpoint-wise alignment of different objects from the same category. Overall, our work demonstrates that embodied interactions with obj
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,BAFFLE: A Baseline of?Backpropagation-Free Federated Learning,BAFFLE only execute forward propagation and return a set of scalars to the server. Empirically we use BAFFLE to train deep models from scratch or to finetune pretrained models, achieving acceptable results.
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,Omni6DPose: A Benchmark and?Model for?Universal 6D Object Pose Estimation and?Tracking,d ambiguities. To address this issue, we introduce ., an enhanced version of the?SOTA category-level 6D object pose estimation framework, incorporating two pivotal improvements: Semantic-aware feature extraction?and Clustering-based aggregation. Moreover, we provide a comprehensive benchmarking anal
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,Style-Extracting Diffusion Models for?Semi-supervised Histopathology Segmentation,, by leveraging styles from unseen images, resulting in more diverse generations.?In this work, we use the image layout as target condition and?first show the capability of our method on a natural image dataset as?a proof-of-concept. We further demonstrate its versatility?in histopathology, where we
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