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TriDet: Temporal Action Detection With Relative Boundary Modeling

CVPR 2023 · Video › Action recognition · approach: Fully supervised temporal action detection · 213 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use relative boundary distributions with Trident-head instead of instant-level boundary regression (ablation)TriDet with SGP · THUMOS14average mAP: 68.3 -> 69.3Helped
replace self-attention with SGP (ablation)TriDet feature pyramid · THUMOS14average mAP: 66.8 -> 68.3Helped
replace the two-layer convolutional baseline pyramid with SGP (ablation)TriDet feature pyramid · THUMOS14average mAP: 62.1 -> 68.3Helped
replace self-attention with a 1D convolution using the same receptive field (ablation)Transformer baseline · THUMOS14average mAP: 66.8 -> 64.9Hurt
replace Layer Normalization with Group Normalization (ablation)Transformer baseline · THUMOS14average mAP: 66.8 -> 64.8Hurt
use a hybrid of Layer Normalization and Group Normalization (ablation)Transformer baseline · THUMOS14average mAP: 66.8 -> 65.7Hurt
add Trident-head to LN-GN-Mix (ablation)LN-GN-Mix convolutional structure · THUMOS14average mAP: 65.7 -> 66.0Helped
use SlowFast instead of I3D featuresTriDet · HACS-Segmentaverage mAP: 36.8 -> 38.6Helped
increase the number of boundary bins from 4 to 14 (ablation)TriDet · HACS-Segmentaverage mAP: 33.3 -> 38.6Helped
increase the number of boundary bins from 4 to 16 (ablation)TriDet · THUMOS14average mAP: 68.1 -> 69.3Helped
increase feature pyramid levels from 1 to 6 (ablation)TriDet · THUMOS14average mAP: 44.5 -> 69.3Helped
increase feature pyramid levels from 6 to 7 (ablation)TriDet · THUMOS14average mAP: 69.3 -> 68.9Hurt

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