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PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points

NeurIPS 2022 · Video › Action recognition · approach: Fully supervised temporal action detection · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace segment-based RoI-align features with learnable query points (ablation)PointTAD · MultiTHUMOSdetection-mAPHelped
use deformable convolution instead of direct interpolation at query points (ablation)PointTAD · MultiTHUMOSdetection-mAPHelped
add frame mixing to channel-only mixing (parallel) (ablation)PointTAD · MultiTHUMOSavg detection-mAPHelped
cascade frame->channel mixing instead of parallel/single (ablation)PointTAD · MultiTHUMOSavg detection-mAP: -1% avg-mAPHurt
cascade channel->frame mixing instead of single (ablation)PointTAD · MultiTHUMOSavg detection-mAP: -3% avg-mAPHurt
partial min-max vs min-max transformation (ablation)PointTAD · MultiTHUMOSavg detection-mAPNo clear effect
vary query point initialization (uniform, normal, constant) (ablation)PointTAD · MultiTHUMOSavg detection-mAPNo clear effect
increase number of query points N_s up to 21 (ablation)PointTAD · MultiTHUMOSdetection-mAPHelped
increase input resolution from 96^2 to 128^2 (ablation)PointTAD · MultiTHUMOSavg detection-mAP: +3.9% absoluteHelped
increase input resolution from 128^2 to 160^2 (ablation)PointTAD · MultiTHUMOSavg detection-mAP: +1% absoluteHelped
increase input resolution from 160^2 to 192^2 (ablation)PointTAD · MultiTHUMOSavg detection-mAP: +0.6% absoluteHelped
scale offsets by action duration instead of clip duration (ablation)PointTAD · MultiTHUMOSdetection-mAPHelped

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