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Representation Learning via Global Temporal Alignment and Cycle-Consistency

CVPR 2021 · Video › Action recognition · approach: Temporal action segmentation · 55 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the contrastive cost (ablation)SmoothDTW · FineGym101accuracy: 28.20 -> 47.32Helped
Add GCC when contrastive cost is absent (ablation)SmoothDTW · FineGym101accuracy: 28.20 -> 28.20No clear effect
Add GCC (ablation)SmoothDTW with contrastive cost · FineGym101accuracy: 47.32 -> 49.51Helped
Replace smoothMin with minγ (ablation)SmoothDTW with contrastive cost and GCC · FineGym101accuracy: 49.51 -> 48.07Hurt
Use global temporal alignment rather than independently matched frame correspondencesFineGym101, scratch · FineGym101, scratchaccuracy: 41.78 -> 45.79Helped
Use global temporal alignment rather than independently matched frame correspondencesFineGym290, scratch · FineGym290, scratchaccuracy: 40.57 -> 43.49Helped
Use global temporal alignment rather than independently matched frame correspondencesFineGym101, only BN · FineGym101, only BNaccuracy: 45.62 -> 49.51Helped
Use global temporal alignment rather than independently matched frame correspondencesFineGym290, only BN · FineGym290, only BNaccuracy: 43.40 -> 46.54Helped
Use global temporal alignment rather than independently matched frame correspondencesPennAction joint representation · PennAction, 13 non-repetitive classesKendall's Tau: 71.0 -> 74.84Helped
Use global temporal alignment rather than independently matched frame correspondencesPennAction joint representation · PennAction, 13 non-repetitive classesPhase classification: 77.51 -> 78.90Helped

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