Alacrity
发表于 2025-3-21 17:10:02
书目名称Computer Vision – ECCV 2022影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0234275<br><br> <br><br>书目名称Computer Vision – ECCV 2022读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0234275<br><br> <br><br>
战胜
发表于 2025-3-21 23:16:09
,Constructing Balance from Imbalance for Long-Tailed Image Recognition,the separability of head-tail classes varies among different features with different inductive biases. Hence, our proposed model also provides a . method and paves the way for long-tailed . learning. Extensive experiments show that our method can boost the performance of state-of-the-arts of differe
Mosaic
发表于 2025-3-22 03:43:04
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要塞
发表于 2025-3-22 07:28:53
,Worst Case Matters for Few-Shot Recognition,o reduce the bias. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed strategies, which outperforms current state-of-the-art methods with a significant margin in terms of not only average, but also worst-case accuracy.
gerontocracy
发表于 2025-3-22 10:40:50
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FLAT
发表于 2025-3-22 16:05:23
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FLAT
发表于 2025-3-22 18:16:38
,Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation,dot-product attention in the Transformer architecture, DCAMA treats every query pixel as a token, computes its similarities with all support pixels, and predicts its segmentation label as an additive aggregation of all the support pixels’ labels—weighted by the similarities. Based on the unique form
Fecal-Impaction
发表于 2025-3-23 00:35:05
,Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning, alleviate the limited diversity problem. Finally, our approach is also model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchma
褪色
发表于 2025-3-23 03:17:07
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Ardent
发表于 2025-3-23 07:36:57
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