Titlebook: Computer Vision – ECCV 2022 Workshops; Tel Aviv, Israel, Oc Leonid Karlinsky,Tomer Michaeli,Ko Nishino Conference proceedings 2023 The Edit

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書目名稱Computer Vision – ECCV 2022 Workshops影響因子(影響力)




書目名稱Computer Vision – ECCV 2022 Workshops影響因子(影響力)學(xué)科排名




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書目名稱Computer Vision – ECCV 2022 Workshops網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Computer Vision – ECCV 2022 Workshops被引頻次學(xué)科排名




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書目名稱Computer Vision – ECCV 2022 Workshops年度引用學(xué)科排名




書目名稱Computer Vision – ECCV 2022 Workshops讀者反饋




書目名稱Computer Vision – ECCV 2022 Workshops讀者反饋學(xué)科排名

Microaneurysm

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

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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 ..

書法

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

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

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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.

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