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Space-time Mixing Attention for Video Transformer

NeurIPS 2021 · Video › Video segmentation and tracking · approach: Self-supervised video representation learning · 58 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add space-time mixing attention with window tw=1 (vs spatial-only tw=0) (ablation)X-ViT ViT-B/16 temporal avg · SSv2top-1 accuracy: 45.2 -> 62.5Helped
increase local window from tw=1 to tw=2 (ablation)X-ViT · SSv2top-1 accuracy: 62.5 -> 60.5Hurt
apply space-time mixing to all layers instead of half (ablation)X-ViT · SSv2top-1 accuracy: 61.7 -> 62.6Helped
replace temporal averaging with one Temporal Attention layer (ablation)X-ViT · SSv2top-1 accuracy: 62.4 -> 64.4Helped
increase TA layers from 1 to 3 (ablation)X-ViT · SSv2top-1 accuracy: 64.4 -> 64.5No clear effect
add space-time mixing to spatial-only attention with TA (ablation)X-ViT · SSv2top-1 accuracy: 56.6 -> 64.4Helped
mix key and value instead of input token or value only (ablation)X-ViT · SSv2top-1 accuracy: 63.1 -> 64.4Helped
mix 100% of channels instead of 50% (ablation)X-ViT · SSv2top-1 accuracy: 64.4 -> 62.5Hurt
use summary token instead of TA (ablation)X-ViT · SSv2top-1 accuracy: 64.4 -> 63.7Hurt
use summary token instead of TA (ablation)X-ViT · Kinetics-400top-1 accuracy: 78.5 -> 78.7Helped
use ViT-B/16 instead of ViT-B/32 (more tokens) (ablation)X-ViT · SSv2top-1 accuracy: 60.5 -> 64.4Helped
increase frames from 8 to 16X-ViT · SSv2top-1 accuracy: 64.4 -> 66.2Helped

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