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Research map / Detection and segmentation / Tracking

Transformer Tracking With Cyclic Shifting Window Attention

CVPR 2022 · Detection and segmentation › Tracking · approach: Foundation-model and correlation-filter tracking · 237 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace pixel-level attention with window attention alone (ablation)Transformer tracker · UAV123AUC: 66.2 -> 54.4Hurt
add cyclic shifts to window attention (ablation)CSWinTT · UAV123AUC: 54.4 -> 69.7Helped
add the spatially regularized attention mask (ablation)CSWinTT with window attention and cyclic shifts · UAV123AUC: 69.7 -> 70.1Helped
add relative position encoding without the spatial mask (ablation)CSWinTT with window attention and cyclic shifts · UAV123AUC: 69.7 -> 69.8Helped
combine the spatial mask and relative position encoding (ablation)CSWinTT with window attention and cyclic shifts · UAV123AUC: 70.1 -> 70.5Helped
use multi-scale windows rather than one fixed window size (ablation)CSWinTT · UAV123AUC: 70.0 -> 70.5Helped
remove cyclic shifts of Query (ablation)CSWinTT computation · Tracking speedFPS: 1.0 -> 8.2Helped
halve the shifting periods (ablation)CSWinTT computation with Query shifts removed · Tracking speedFPS: 8.2 -> 10.9Helped
use coordinate permutations instead of direct matrix translations for cyclic shifts (ablation)CSWinTT computation with Query shifts removed and periods halved · Tracking speedFPS: 10.9 -> 12.4Helped
use cyclic-shifting window attention instead of the original transformer (ablation)CSWinTT · Tracking speedFPS: 14.9 -> 12.4Hurt

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