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Research map / Architectures and efficiency / Attention and transformers

Attentional Pooling for Action Recognition

NeurIPS 2017 · Architectures and efficiency › Attention and transformers · approach: Sparse and structured attention · 209 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace average pooling with attentional poolingResNet101 · MPII ValmAP: 26.2 -> 30.3Helped
Add pose regularization to attentional pooling (ablation)ResNet101 · MPII ValmAP: 30.3 -> 30.6Helped
Replace average pooling with attentional poolingResNet101 · HICOmAP: 30.2 -> 35.0Helped
Add pose regularization to attentional pooling (ablation)ResNet101 · HICOmAP: 35.0 -> 34.6Hurt
Replace average pooling with attentional poolingTSN ResNet101 · HMDB51 Avgaccuracy: 47.1 -> 50.8Helped
Add pose regularization to attentional pooling (ablation)TSN ResNet101 · HMDB51 Avgaccuracy: 50.8 -> 52.2Helped
Replace average pooling with attentional poolingInception-V2 · MPII ValmAP: 25.2 -> 24.3Hurt
Use compact bilinear pooling instead of average pooling (ablation)ResNet101 · HICOmAP: 30.2 -> 26.8Hurt
Increase attention rank from 1 to 2 (ablation)Attentional Pooling · MPII ValmAP: 30.3 -> 29.9Hurt
Increase attention rank from 1 to 5 (ablation)Attentional Pooling · MPII ValmAP: 30.3 -> 30.0Hurt
Use class-specific bottom-up attention maps (ablation)Attentional Pooling · MPII ValmAP: 30.3 -> 27.9Hurt
Increase input resolution from 224px to 450px (ablation)HMDB51 TSN ResNet · HMDB51accuracyHelped

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