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Compact Transformer Tracker with Correlative Masked Modeling

AAAI 2023 · Architectures and efficiency › Attention and transformers · approach: Block-sparse attention kernels · 104 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add all three correlative reconstruction streams (s2s, t2t, and s2t) (ablation)CTTrack-B · LaSOTAUC: 64.0 -> 65.8Helped
Add search-token self-reconstruction (s2s) (ablation)CTTrack-B · LaSOTAUC: 64.0 -> 64.7Helped
Add template-token self-reconstruction (t2t) (ablation)CTTrack-B · LaSOTAUC: 64.0 -> 64.4Helped
Add search-to-template reconstruction (s2t) (ablation)CTTrack-B · LaSOTAUC: 64.0 -> 64.4Helped
Use a 75% masking ratio rather than a 25% ratio (ablation)CTTrack-B · LaSOTAUC: 64.6 -> 65.8Helped
Increase masking ratio from 75% to 90% (ablation)CTTrack-B · LaSOTAUC: 65.8 -> 64.9Hurt
Add fixed-interval online template updating without confidence filtering (ablation)CTTrack-B · LaSOTAUC: 65.8 -> 64.9Hurt
Add confidence-based score-head filtering to online template updating (ablation)CTTrack-B with fixed-interval updating · LaSOTAUC: 64.9 -> 66.0Helped
Remove template cross-information stream 2 from packed self-attention (ablation)CTTrack · LaSOTAUC: 61.7 -> 64.0Helped
Remove template self-information stream 1 from packed self-attention (ablation)CTTrack · LaSOTAUC: 61.7 -> 60.6Hurt
Remove search self-information stream 4 from packed self-attention (ablation)CTTrack · LaSOTAUC: 61.7 -> 58.8Hurt
Use ViT-Large instead of ViT-Base (ablation)CTTrack · LaSOTAUC: 67.8 -> 69.8Helped

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