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Cross-Architecture Self-Supervised Video Representation Learning

CVPR 2022 · Video › Action recognition · approach: Contrastive video representation learning · 23 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add cross-architecture contrastive learning with a video transformer (ablation)V-MoCo with C3D · UCF101 video retrievalRecall@1 (%): 30.3 -> 39.1Helped
Add shuffle-degree prediction to video-level contrastive learning (ablation)V-MoCo with C3D · UCF101 video retrievalRecall@1 (%): 30.3 -> 36.2Helped
Add the transformer and shuffle-degree prediction together (ablation)V-MoCo with C3D · UCF101 video retrievalRecall@1 (%): 30.3 -> 43.2Helped
Use uniform shuffle-degree sampling (ablation)Shuffle-degree prediction with C3D · UCF101 video retrievalRecall@1 (%): 17.2 -> 22.1Helped
Add progressively more groups of cross-architecture positive pairs (ablation)CACL with C3D · UCF101 video retrievalRecall@1 (%): 36.2 -> 37.8 -> 39.3 -> 43.2Helped
Pretrain on Kinetics400 rather than UCF101 (ablation)CACL with C3D · UCF101 video retrievalRecall@1 (%): 43.2 -> 44.2Helped
Use CACL rather than the strongest listed C3D retrieval baselineC3D video representations · UCF101 video retrievalRecall@1 (%): 36.0 -> 43.2Helped
Use CACL rather than the strongest listed C3D retrieval baselineC3D video representations · HMDB51 video retrievalRecall@1 (%): 8.2 -> 17.3Helped
Use CACL rather than the strongest listed methodR(2+1)D action recognition · HMDB51Top-1 accuracy (%): 49.2 -> 48.8Hurt

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