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Research map / Detection and segmentation / CNN features

Fast and Unsupervised Action Boundary Detection for Action Segmentation

CVPR 2022 · Detection and segmentation › CNN features · approach: Efficient DETR query design · 45 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
detect boundaries from smoothed-frame similarity and refine segmentsABD · BreakfastMoF: 62.7 -> 64.0Helped
detect boundaries from smoothed-frame similarity and refine segmentsABD · YouTube Instructional VideosMoF: 56.7 -> 67.2Helped
detect boundaries from smoothed-frame similarity and refine segmentsABD · Hollywood ExtendedMoF: 55.0 -> 60.7Helped
detect boundaries from smoothed-frame similarity and refine segmentsABD · 50Salads midMoF: 66.5 -> 71.8Helped
add feature smoothing (ablation)ABD · BreakfastMoF: 58.3 -> 64.0Helped
use Gaussian rather than average feature smoothing (ablation)ABD · BreakfastMoF: 63.5 -> 64.0No clear effect
add non-maximum suppression (ablation)ABD · BreakfastMoF: 57.3 -> 64.0Helped
add segment clustering refinement (ablation)ABD · BreakfastMoF: 57.8 -> 64.0Helped
include temporal information in refinement (ablation)ABD · BreakfastMoF: 64.0 -> 64.2No clear effect
include temporal information in refinement (ablation)ABD · YouTube Instructional Videos and Hollywood ExtendedMoF: 67.2 -> 66.4; 60.7 -> 60.5Hurt
include temporal information in refinement (ablation)ABD · 50Salads midMoF: 71.8 -> 72.2No clear effect
use I3D rather than Fisher Vector features (ablation)ABD · BreakfastMoF: 64.0 -> 65.4Helped

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