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Self-Supervised Spatiotemporal Representation Learning by Exploiting Video Continuity

AAAI 2022 · Video › Action recognition · approach: Contrastive video representation learning · 20 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use all three continuity-aware pretext tasks jointly (ablation)CPNet · UCF101, HMDB51, Diving48top-1 accuracy: 80.7 vs. 78.4; 51.8 vs. 47.2; 72.5 vs. 71.0Helped
Add discontinuity localization (ablation)CPNet · UCF101top-1 accuracy: 74.3 -> 78.4Helped
Add continuity justification (ablation)CPNet · UCF101top-1 accuracy: 75.5 -> 78.6Helped
Add missing section approximation (ablation)CPNet · UCF101top-1 accuracy: 78.4 -> 78.6Helped
Add triplet loss to missing-section contrastive learning (ablation)CPNet · UCF101top-1 accuracy: 80.0 -> 80.7Helped
Set missing-section length to 8 rather than 4 or 16 (ablation)CPNet · UCF101, HMDB51, Diving48top-1 accuracy: 80.1, 49.9, 72.1 -> 80.7, 51.8, 72.5; length 16: 79.8, 47.3, 71.6Helped
Add discontinuity localization to prior self-supervised methodsPacePred, VCOP, and MOCO · UCF101 and HMDB51top-1 accuracy: PacePred: +0.6 to +3.9 UCF101 and +1.7 to +3.3 HMDB51; VCOP: +1.3 to +2.2 UCF101 and +0.5 to +3.2 HMDB51; MOCO: +0.6 to Helped
Pretrain CPNet on K400 rather than UCF101CPNet with R(2+1)D · UCF101 and HMDB51top-1 accuracy: 81.8 -> 83.8; 51.2 -> 57.1Helped
Evaluate K400-pretrained CPNet at 15 fps rather than 25 fps (ablation)CPNet with R(2+1)D · UCF101 and HMDB51top-1 accuracy: 82.1 -> 83.8; 53.2 -> 57.1Helped
Use feature-space contrastive missing-section approximation rather than RGB reconstruction (ablation)CPNet versus CRNet · UCF101/HMDB51 video retrieval, R(2+1)D R@1R@1: 35.3/14.0 vs. 22.9/12.0Helped

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