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Weakly Supervised Action Learning With RNN Based Fine-To-Coarse Modeling

CVPR 2017 · Video › Action recognition · approach: Temporal action segmentation · 207 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add subaction modeling (ablation)GRU weakly supervised action segmentation · BreakfastAccuracy (Mof): 22.4 -> 33.3Helped
Add subaction-count reestimation during iterative training (ablation)GRU with subactions · Breakfast temporal action segmentationAccuracy (Mof): 28.8 -> 33.3Helped
Set subaction counts using ground-truth action lengths (ablation)GRU with subactions · Breakfast temporal action segmentationAccuracy (Mof): 33.3 -> 51.3Helped
Use a recurrent GRU instead of a GMM or MLP for fine-grained classification (ablation)Weakly supervised subaction model · BreakfastAccuracy (Mof): GMM: 15.3, 23.3, 26.3, 27.0, 26.5; MLP: 22.4, 24.0, 23.7, 23.1, 20.3; GRU: 25.5, 29.1, 28.6, 29.3, 28.8Helped
Add the coarse HMM model (ablation)GRU fine-grained action classifier · BreakfastAccuracy (Mof): 21.3, 20.1, 23.8, 21.8 -> 25.5, 29.1, 28.6, 29.3Helped
Add subaction-count reestimation (ablation)GRU weakly supervised segmentation · Hollywood Ext. temporal action segmentationJacc. (IoU): 11.2 -> 11.9Helped
Add subaction-count reestimation (ablation)GRU weakly supervised action alignment · BreakfastJacc. (IoD): 41.5 -> 47.3Helped
Add subaction-count reestimation (ablation)GRU weakly supervised action alignment · Hollywood Ext.Jacc. (IoD): 45.6 -> 46.3Helped

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