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Action Unit Memory Network for Weakly Supervised Temporal Action Localization

CVPR 2021 · Video › Action recognition · approach: Weakly supervised temporal action localization · 98 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use AUMN instead of TSCNWeakly supervised temporal action localization · THUMOS14 testaverage mAP@IoU (0.1:0.1:0.5): 47.0 -> 52.1Helped
Use AUMN instead of Bas-NetWeakly supervised temporal action localization · ActivityNet1.2 validationaverage mAP@IoU: 24.3 -> 25.5Helped
Use AUMN instead of A2CL-PTWeakly supervised temporal action localization · ActivityNet1.3 validationaverage mAP@IoU: 22.5 -> 23.5Helped
Add sparsity loss (ablation)AUMN · THUMOS14average mAP@IoU (0.1:0.1:0.5): 43.2 -> 49.2Helped
Add diversity loss to the sparsity-loss model (ablation)AUMN · THUMOS14average mAP@IoU (0.1:0.1:0.5): 49.2 -> 50.2Helped
Add homogeneity loss to the sparsity-loss model (ablation)AUMN · THUMOS14average mAP@IoU (0.1:0.1:0.5): 49.2 -> 49.8Helped
Add diversity and homogeneity losses to sparsity loss (ablation)AUMN · THUMOS14average mAP@IoU (0.1:0.1:0.5): 49.2 -> 51.4Helped
Add self-attention to the model with all three auxiliary losses (ablation)AUMN · THUMOS14average mAP@IoU (0.1:0.1:0.5): 51.4 -> 52.1Helped
Add diversity and homogeneity losses together rather than using either alone with sparsity (ablation)AUMN · THUMOS14mAP@IoU 0.5: 28.8; 29.5; 29.4 -> 31.8Helped

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