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

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书目名称Computer Vision – ECCV 2024影响因子(影响力)




书目名称Computer Vision – ECCV 2024影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2024网络公开度




书目名称Computer Vision – ECCV 2024网络公开度学科排名




书目名称Computer Vision – ECCV 2024被引频次




书目名称Computer Vision – ECCV 2024被引频次学科排名




书目名称Computer Vision – ECCV 2024年度引用




书目名称Computer Vision – ECCV 2024年度引用学科排名




书目名称Computer Vision – ECCV 2024读者反馈




书目名称Computer Vision – ECCV 2024读者反馈学科排名

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Jogging

,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

串通

,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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legislate

,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

削减

,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