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Research map / Architectures and efficiency / RNNs and normalization

Recurrent Modeling of Interaction Context for Collective Activity Recognition

CVPR 2017 · Architectures and efficiency › RNNs and normalization · approach: LSTM design and memory · 173 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use hierarchical recurrent interaction-context modeling instead of pooling-SVMCollective activity recognition · Collective Activity DatasetMPCA (%): 85.8 -> 89.4Helped
Use hierarchical recurrent interaction-context modelingCollective activity recognition · Collective Activity DatasetMPCA (%): 90.8 -> 89.4Hurt
Use recurrent interaction-context modeling with motion threshold µ = 0Group activity recognition · Choi’s New DatasetMCA (%): 83.0 -> 89.4Helped
Use recurrent interaction-context modeling with motion threshold µ = 1Group activity recognition · Choi’s New DatasetMPCA (%): 80.3 -> 85.2Helped
Set motion threshold µ to 1 pixel instead of 0 (ablation)Recurrent interaction-context model · Choi’s New DatasetMCA (%): 89.4 -> 89.2Hurt
Set motion threshold µ to 1 pixel instead of 0 (ablation)Recurrent interaction-context model · Choi’s New DatasetMPCA (%): 84.6 -> 85.2Helped
Set motion threshold µ to 2 pixels instead of 0 (ablation)Recurrent interaction-context model · Choi’s New DatasetMCA (%): 89.4 -> 87.3Hurt
Set motion threshold µ to 2 pixels instead of 0 (ablation)Recurrent interaction-context model · Choi’s New DatasetMPCA (%): 84.6 -> 84.5No clear effect

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