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Research map / Video / Action recognition

Modular Action Concept Grounding in Semantic Video Prediction

CVPR 2022 · Video › Action recognition · approach: Temporal action segmentation · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use language-guided concept slots instead of concatenated features and a tiled action vector (ablation)MAC · CLEVR-Building-blocksAccuracy: 50.8% -> 95.2%Helped
Use language-guided concept slots instead of concatenated features and a tiled action vector (ablation)MAC · Sapien-KitchenAccuracy: 52.4% -> 86.4%Helped
Use language-guided concept slots instead of concatenated features and a tiled action vector (ablation)MAC · CLEVR-Building-blocksMSE: 226.53 -> 43.52Helped
Use language-guided concept slots instead of concatenated features and a tiled action vector (ablation)MAC · Sapien-KitchenMSE: 23.13 -> 11.16Helped
Use MAC rather than AG2Vid, which requires bounding boxessemantic action-conditional video prediction · CLEVR-Building-blocksAccuracy: 78.8% -> 95.2%Helped
Use MAC rather than AG2Vid, which requires bounding boxessemantic action-conditional video prediction · Sapien-KitchenAccuracy: 5.2% -> 86.4%Helped
Use stochastic MAC with concept slots rather than action-conditional SVG-LPstochastic video prediction · Tower-CreationAccuracy: 31.8% -> 65.3%Helped
Train without red-cube videos, then test instructions involving red cubesMAC · CLEVR-Building-blocks, unobserved red-cube scenariosAccuracy: 75.6%Helped

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