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发表于 2025-3-21 19:38:57
书目名称Computer Vision – ECCV 2024影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0242345<br><br> <br><br>书目名称Computer Vision – ECCV 2024读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0242345<br><br> <br><br>
大漩涡
发表于 2025-3-21 21:46:14
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FLASK
发表于 2025-3-22 03:48:44
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Jogging
发表于 2025-3-22 06:25:35
,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
发表于 2025-3-22 10:16:15
,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.
精致
发表于 2025-3-22 14:19:03
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精致
发表于 2025-3-22 18:07:22
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渗透
发表于 2025-3-22 22:45:20
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legislate
发表于 2025-3-23 01:53:06
,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
发表于 2025-3-23 07:12:58
,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