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DC-Former: Diverse and Compact Transformer for Person Re-identification

AAAI 2023 · Label-efficient and robust learning › Recognition and long-tail · approach: Cross-modality and occlusion re-identification · 72 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DC-Former instead of TransReIDViT-B/16 person re-ID · MSMT17mAP: 67.4 -> 69.8Helped
Use DC-Former instead of AAformerViT-B/16 person re-ID · CUHK03-lmAP: 77.8 -> 79.4Helped
Add a second class token without SDC (ablation)Baseline · MSMT17mAP: 66.1 -> 66.9Helped
Add SDC to two class tokens (ablation)Two-token model · MSMT17mAP: 66.9 -> 68.3Helped
Add SDC to two class tokens (ablation)Two-token model · MSMT17Rank-1: 84.8 -> 85.5Helped
Concatenate two SDC-trained token embeddings instead of using one token (ablation)DC-Former · MSMT17mAP: 67.9 -> 68.3Helped
Set SDC weight λ to 1 rather than 0 (ablation)Two-token DC-Former · MSMT17mAP: 66.9 -> 68.3Helped
Increase λ beyond 1 (ablation)Two-token DC-Former · MSMT17mAPHurt
Increase class-token count to N = 4 (ablation)DC-Former · MSMT17mAP: 69.2%Helped
Increase class-token count beyond the useful range (ablation)DC-Former · MSMT17mAPHurt
Use DWC when token count is large (ablation)DC-Former · MSMT17mAP: +1.0%Helped
Use DWC when token count is small (less than 5) (ablation)DC-Former · MSMT17mAPNo clear effect

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