里程表
发表于 2025-3-21 16:13:23
书目名称Computer Vision – ECCV 2022影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0234247<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=BK0234247<br><br> <br><br>书目名称Computer Vision – ECCV 2022网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0234247<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=BK0234247<br><br> <br><br>书目名称Computer Vision – ECCV 2022被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0234247<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=BK0234247<br><br> <br><br>书目名称Computer Vision – ECCV 2022年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0234247<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=BK0234247<br><br> <br><br>书目名称Computer Vision – ECCV 2022读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0234247<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=BK0234247<br><br> <br><br>
PLUMP
发表于 2025-3-21 23:33:24
,Constrained Mean Shift Using Distant yet Related Neighbors for Representation Learning,s like mean-shift (MSF) cluster images by pulling the embedding of a query image to be closer to its nearest neighbors (NNs). Since most NNs are close to the query by design, the averaging may not affect the embedding of the query much. On the other hand, far away NNs may not be semantically related
chlorosis
发表于 2025-3-22 03:45:41
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Limerick
发表于 2025-3-22 07:39:06
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Accord
发表于 2025-3-22 09:37:50
,Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation, desirable due to the heavy burden of collecting abundant dense annotations for the model training. However, existing methods remain challenging to accurately segment 3D point clouds since limited annotated data may lead to insufficient guidance for label propagation to unlabeled data. Considering t
NAG
发表于 2025-3-22 14:52:42
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NAG
发表于 2025-3-22 20:10:47
Self-Supervised Classification Network,multaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entro
高度
发表于 2025-3-23 00:31:21
Data Invariants to Understand Unsupervised Out-of-Distribution Detection, applicability over its supervised counterpart. Despite this increased attention, U-OOD methods suffer from important shortcomings. By performing a large-scale evaluation on different benchmarks and image modalities, we show in this work that most popular state-of-the-art methods are unable to consi
ORE
发表于 2025-3-23 04:25:43
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains,ile recent self-supervised learning methods have achieved good performances with evaluation set on the same domain as the training set, they will have an undesirable performance decrease when tested on a different domain. Therefore, the self-supervised learning from multiple domains task is proposed
苦涩
发表于 2025-3-23 05:33:36
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