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Generalized Relation Modeling for Transformer Tracking

CVPR 2023 · Architectures and efficiency › Attention and transformers · approach: Transformer block and depth design · 247 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace the two-stream relation pipeline with GRM adaptive token division (ablation)Transformer tracking · GOT-10k*AO: 64.7 -> 73.4Helped
Allow early template-search interaction for all search tokens (one-stream instead of two-stream) (ablation)Transformer tracking · GOT-10k*AO: 64.7 -> 71.0Helped
Add an additional shared search-token category E_A to binary token division (ablation)GRM · GOT-10k*AO: 71.5 -> 71.9Helped
Divide search tokens into E_S and E_A, preserving interaction among all search tokens while selecting template-interacting tokens (ablation)GRM · GOT-10k*AO: 71.9 -> 73.4Helped
Apply token division in the first encoder layer (ablation)GRM · GOT-10k*AO: 73.4 -> 71.2Hurt
Restrict token division to layers 5–12 rather than layers 2–12 (ablation)GRM · GOT-10k*AO: 73.4 -> 72.3Hurt
Use maximum pooling rather than average pooling for the target-aware representation (ablation)GRM · GOT-10k*AO: 71.8 -> 73.4Helped
Use attention masking to parallelize the attention operations (ablation)GRM inference · Tracking inferencefps: 33 -> 45Helped
Use GRM instead of OSTrackTransformer tracking · GOT-10k*AO: 71.0 -> 73.4Helped
Use GRM instead of OSTrackTransformer tracking · TrackingNetAUC: 83.1 -> 84.0Helped
Use GRM instead of OSTrackTransformer tracking · LaSOTAUC: 69.1 -> 69.9Helped
Use GRM instead of MixFormerTransformer tracking · AVisTAUC: 53.7 -> 54.5Helped

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