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Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis

AAAI 2024 · Video › Video segmentation and tracking · approach: Self-supervised video representation learning · 6 citations (OpenAlex)

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Proposes Collaborative Procedure Alignment (CPA), a weakly supervised framework that jointly mines step segmentations for paired instructional videos via dynamic programming and aligns frames to steps to measure video correlation, evaluated on sequence verification and action quality assessment.

Problem

Procedure-aware correlation learning on instructional videos normally requires costly step-level annotations (step labels and boundaries); the paper targets learning procedural knowledge using only video-level class labels.

Approach

Collaborative step mining uses dynamic programming over the frame-to-frame similarity matrix of paired videos to produce consistent step segmentations for both videos simultaneously, and frame-to-step alignment computes a DP-based alignment probability (negative log-likelihood as distance) in both directions to measure procedural correlation.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use CPA+R50 instead of the strongest listed baselineSequence verification · CSVAUC: 86.92 -> 88.14Helped
Use CPA+X3D instead of the strongest listed baselineSequence verification · Diving-SVAUC: 86.09 -> 88.11Helped
Use CPA+R50 instead of WeakSVRSequence verification · COIN-SVAUC: 59.57 -> 57.57Hurt
Replace supervised TSA segmentation with CPA segmentationTSA on FineDiving · FineDivingAIoU@0.5: 93.23 -> 94.28Helped
Replace supervised TSA segmentation with CPA segmentationTSA on FineDiving · FineDivingAIoU@0.75: 53.39 -> 21.14Hurt
Replace supervised TSA segmentation with CPA segmentationTSA on FineDiving · FineDivingSpearman's ρ: 0.9302 -> 0.9364Helped
Replace supervised TSA segmentation with CPA segmentationTSA on FineDiving · FineDivingR-ℓ2 (× 100): 0.3154 -> 0.2909Helped
Add FSA to the video-level feature verification setup (ablation)CPA on CSV · CSVAUC: 81.65 -> 86.23Helped
Add CSM to FSA with uniformly sampled step features (ablation)CPA on CSV · CSVAUC: 86.23 -> 88.14Helped
Add FSA to uniformly divided video segments (ablation)CPA on FineDiving · FineDivingSpearman's ρ: 0.9221 -> 0.9275Helped
Add FSA to uniformly divided video segments (ablation)CPA on FineDiving · FineDivingR-ℓ2: 0.3456 -> 0.3288Helped
Add CSM to FSA with uniformly sampled step features (ablation)CPA on FineDiving · FineDivingSpearman's ρ: 0.9275 -> 0.9364Helped

Surprising findings

Limitations

Experimental setup

Datasets: CSV Diving-SV COIN-SV FineDiving Kinetics-400

Benchmarks: CSV Diving-SV COIN-SV FineDiving

Metrics: AUC Spearman's rho relative L2 distance AIoU

Baselines: TRN TSM Video-Swin CAT OTAM TAP Drop-DTW CLIP+TE+MLP WeakSVR USDL MUSDL CoRe

Builds on: TSA OTAM Drop-DTW

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