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G-TAD: Sub-Graph Localization for Temporal Action Detection

CVPR 2020 · Video › Action recognition · approach: Fully supervised temporal action detection · 489 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add semantic graph convolution to GCNeXt (ablation)G-TAD · ActivityNet-1.3 validationaverage mAP: 32.63 -> 34.09Helped
Add temporal graph convolution to GCNeXt (ablation)G-TAD · ActivityNet-1.3 validationaverage mAP: 33.67 -> 34.09Helped
Increase GCNeXt cardinality from 1 to 32 with both graph streams (ablation)G-TAD · ActivityNet-1.3 validationaverage mAP: 33.88 -> 34.09Helped
Add semantic feature concatenation in SGAlign (ablation)G-TAD · ActivityNet-1.3 validationaverage mAP: 33.89 -> 34.09Helped
Use sample-rescale alignment (ablation)G-TAD SGAlign without semantic feature concatenation · ActivityNet-1.3 validationaverage mAP: 33.78 -> 33.89Helped
Use the union of semantic edges from all GCNeXt blocks (ablation)G-TAD SGAlign · ActivityNet-1.3 validationaverage mAP: 34.09 -> 33.95Hurt
Replace FC localization layers with 2D convolution (ablation)G-TAD · ActivityNet-1.3 validationaverage mAP: 34.08; 34.10; 33.77No clear effect
Increase input window length from 128 to 256 (ablation)G-TAD · THUMOS-14 validationmAP at IoU@0.5: 38.70 -> 40.16Helped
Increase input window length from 256 to 512 (ablation)G-TAD · THUMOS-14 validationmAP at IoU@0.5: 40.16 -> 34.38Hurt

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