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Learning Temporal Co-Attention Models for Unsupervised Video Action Localization

CVPR 2020 · Video › Action recognition · approach: Weakly supervised temporal action localization · 72 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Iteratively use class-agnostic attention to weight video features during clustering, then retrain localization using the resulting pseudo-labelsACL on THUMOS14 · THUMOS14 testmAP@0.5: 13.0 -> 20.8 -> 25.0Helped
Add the Local-Global feature aggregation block (ablation)ACL model with classification loss on THUMOS14 · THUMOS14 testAvg(0.3:0.7): 21.39 -> 22.16Helped
Add action-background separation loss (ablation)ACL model with Local-Global feature aggregation and classification loss · THUMOS14 testAvg(0.3:0.7): 22.16 -> 23.48Helped
Add cluster-based triplet loss (ablation)ACL model with Local-Global feature aggregation and classification loss · THUMOS14 testAvg(0.3:0.7): 22.16 -> 23.05Helped
Use both action-background separation loss and cluster-based triplet loss (ablation)ACL model with Local-Global feature aggregation and classification loss · THUMOS14 testAvg(0.3:0.7): 22.16 -> 24.68Helped
Use unsupervised action co-localization rather than weak video-level labelsProposed method on THUMOS14 · THUMOS14 testmAP@0.5: 30.1 -> 25.0Hurt
Use unsupervised action co-localization rather than weak video-level labelsProposed method on ActivityNet-1.2 · ActivityNet-1.2 validationAverage mAP: 24.6 -> 21.1Hurt

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