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ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

ICLR 2022 · Video › Video-language understanding · approach: Event and anomaly reasoning in video · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add four reference videos for inferring physical properties (ablation)Target-video-only GNN · ComPhy physical-property labels, massaccuracy: 59.4% -> 90.4%Helped
Add four reference videos for inferring physical properties (ablation)Target-video-only GNN · ComPhy physical-property labels, charge edgeaccuracy: 70.2% -> 90.8%Helped
Concatenate reference-video features (ablation)CNN-LSTM · ComPhy test, factualaccuracy: 46.6 -> 41.9Hurt
Concatenate reference-video features (ablation)MAC · ComPhy test, factualaccuracy: 68.6 -> 65.8Hurt
Concatenate reference-video features (ablation)ALOE · ComPhy test, predictive per optionaccuracy: 65.9 -> 67.9Helped
Concatenate reference-video features (ablation)ALOE · ComPhy test, counterfactual per questionaccuracy: 20.8 -> 22.2Helped
Concatenate reference-video features (ablation)CNN-LSTM · ComPhy test, predictive per optionaccuracy: 59.5 -> 59.6No clear effect
Concatenate reference-video features (ablation)MAC · ComPhy test, predictive per optionaccuracy: 60.2 -> 60.2No clear effect
Explicitly model mass on graph nodes and relative charge on edgesCPL versus NS-DR+ · ComPhy predictive and counterfactual reasoningaccuracyHelped

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