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Rethinking the Faster R-CNN Architecture for Temporal Action Localization

CVPR 2018 · Video › Action recognition · approach: Weakly supervised temporal action localization · 781 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Align each anchor scale's receptive field using multi-tower dilated convolutions (ablation)TAL-Net proposal generation · THUMOS'14 RGB proposals, AN=10AR: 9.4 -> 14.0Helped
Add temporal context around proposals (ablation)TAL-Net proposal generation · THUMOS'14 RGB proposals, AN=10AR: 14.0 -> 15.1Helped
Add temporal context around proposals (ablation)TAL-Net proposal generation · THUMOS'14 flow proposals, AN=10AR: 16.3 -> 17.4Helped
Add temporal context to action classification (ablation)TAL-Net · THUMOS'14 RGB action localization, tIoU=0.5mAP: 28.5 -> 31.9Helped
Add temporal context to action classification (ablation)TAL-Net · THUMOS'14 flow action localization, tIoU=0.5mAP: 37.4 -> 38.2Helped
Fuse RGB and flow logits late rather than concatenating features early (ablation)TAL-Net · THUMOS'14 action localization, tIoU=0.5mAP: 40.8 -> 42.8Helped
Fuse RGB and flow logits late rather than concatenating features early (ablation)TAL-Net · THUMOS'14 action localization, tIoU=0.1mAP: 60.5 -> 59.8Hurt
Use TAL-Net rather than Gao et al.THUMOS'14 action localization · THUMOS'14, tIoU=0.5mAP: 31.0 -> 42.8Helped
Use TAL-Net rather than Dai et al.ActivityNet v1.3 action localization · ActivityNet v1.3 val, tIoU=0.5mAP: 36.44 -> 38.23Helped
Use TAL-Net rather than Shou et al.ActivityNet v1.3 action localization · ActivityNet v1.3 val, tIoU=0.5mAP: 45.30 -> 38.23Hurt

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