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Improving Weakly Supervised Temporal Action Localization by Bridging Train-Test Gap in Pseudo Labels

CVPR 2023 · Label-efficient and robust learning › Semi-supervised learning · approach: Pseudo-labeling for specialized tasks · 26 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use boundary-derived pseudo-labels with Gaussian fusion, LinPro generation, and Δ pseudo labelsRSKP · THUMOS14AVG mAP (0.1:0.7): 46.4 -> 48.3Helped
Use the proposed pseudo-label pipelineRSKP · ActivityNet1.3 validationAVG mAP: 25.1 -> 28.8Helped
Replace NMS with Gaussian weighted fusion during training (ablation)RSKP · THUMOS14 trainingAVG mAP: 44.9 -> 45.6Helped
Use uniform rather than Gaussian fusion (ablation)RSKP · THUMOS14 trainingAVG mAP: 44.9 -> 31.2Hurt
Use exponential rather than Gaussian fusion (ablation)RSKP · THUMOS14 trainingAVG mAP: 45.3 vs 45.6No clear effect
Use t-distribution rather than Gaussian fusion (ablation)RSKP · THUMOS14 trainingAVG mAP: 45.6 vs 45.6No clear effect
Replace NMS with Gaussian weighted fusion during testing (ablation)RSKP · THUMOS14 testingAVG mAP: 44.9 -> 47.1Helped
Use uniform rather than Gaussian fusion (ablation)RSKP · THUMOS14 testingAVG mAP: 44.9 -> 41.5Hurt
Use exponential rather than Gaussian fusion (ablation)RSKP · THUMOS14 testingAVG mAP: 47.1 vs 47.1No clear effect
Use t-distribution rather than Gaussian fusion (ablation)RSKP · THUMOS14 testingAVG mAP: 47.1 vs 47.1No clear effect
Add LinPro pseudo-label generation to NMS-generated boundaries (ablation)RSKP · THUMOS14AVG mAP: 45.1 -> 44.9Hurt
Add Gaussian weighted fusion after LinPro pseudo-label generation (ablation)RSKP · THUMOS14AVG mAP: 44.9 -> 45.6Helped

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