Introduces synthetic ground-truth dataset for CAM evaluation, proposes ARCC composite metric, and RefineCAM method that aggregates layers for higher-resolution maps outperforming baselines.
International journal of computer vision115(3), 211–252 (2015)
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SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.
DBMF integrates scores from text-image and vision branches to improve out-of-distribution detection on endoscopic datasets by up to 24.84% over prior methods.
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How to Evaluate and Refine your CAM
Introduces synthetic ground-truth dataset for CAM evaluation, proposes ARCC composite metric, and RefineCAM method that aggregates layers for higher-resolution maps outperforming baselines.
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SS3D: End2End Self-Supervised 3D from Web Videos
SS3D pretrains an end-to-end feed-forward 3D estimator on filtered YouTube-8M videos via SfM self-supervision, MVS filtering, and expert distillation, delivering stronger zero-shot transfer and fine-tuning than prior self-supervised baselines.
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Distilling Vision Transformers for Distortion-Robust Representation Learning
An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.
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DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection
DBMF integrates scores from text-image and vision branches to improve out-of-distribution detection on endoscopic datasets by up to 24.84% over prior methods.