代表
发表于 2025-3-21 19:14:21
书目名称Computer Vision – ECCV 2022 Workshops影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0234287<br><br> <br><br>书目名称Computer Vision – ECCV 2022 Workshops读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0234287<br><br> <br><br>
Microaneurysm
发表于 2025-3-21 23:19:58
MoQuad: Motion-focused Quadruple Construction for Video Contrastive Learning. By simply applying MoQuad to SimCLR, extensive experiments show that we achieve superior performance on downstream tasks compared to the state of the arts. Notably, on the UCF-101 action recognition task, we achieve 93.7% accuracy after pre-training the model on Kinetics-400 for only 200 epochs, s
折磨
发表于 2025-3-22 03:31:48
On the Effectiveness of ViT Features as Local Semantic Descriptorsily applicable across a variety of domains. We show by extensive qualitative and quantitative evaluation that our simple methodologies achieve competitive results with recent state-of-the-art . methods, and outperform previous unsupervised methods by a large margin. Code is available in ..
书法
发表于 2025-3-22 07:49:30
A Study on Self-Supervised Object Detection Pretrainingby using a contrastive loss, and (2) predicting box coordinates using a transformer, which potentially benefits downstream object detection tasks. We found that these tasks do not lead to better object detection performance when finetuning the pretrained model on labeled data.
circumvent
发表于 2025-3-22 10:06:00
Artifact-Based Domain Generalization of Skin Lesion Models, when evaluating such models in out-of-distribution data, they did not prefer clinically-meaningful features. Instead, performance only improved in test sets that present similar artifacts from training, suggesting models learned to ignore the known set of artifacts. Our results raise a concern tha
带子
发表于 2025-3-22 14:00:11
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带子
发表于 2025-3-22 17:31:57
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learninghighlighting the skin-type bias in skin lesion classification. Extensive experimental evaluation demonstrates the effectiveness of FairDisCo, with fairer and superior performance on skin lesion classification tasks.
货物
发表于 2025-3-22 22:27:43
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FLAX
发表于 2025-3-23 03:50:59
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anchor
发表于 2025-3-23 09:28:55
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