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Research map / Label-efficient and robust learning / Recognition and long-tail

Joint Learning of Single-Image and Cross-Image Representations for Person Re-Identification

CVPR 2016 · Label-efficient and robust learning › Recognition and long-tail · approach: Supervised person re-identification · 414 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Combine SIR and CIR matching scores in the pairwise model (ablation)Pairwise comparison model · CUHK03rank-1 accuracy (%): 37.15 -> 43.36; 35.70 -> 43.36Helped
Combine SIR and CIR matching scores in the triplet model (ablation)Triplet comparison model · CUHK03rank-1 accuracy (%): 43.23 -> 51.33; 43.46 -> 51.33Helped
Combine SIR and CIR matching scores in the pairwise-plus-triplet model (ablation)Combined comparison model · CUHK03rank-1 accuracy (%): 44.35 -> 52.17; 45.40 -> 52.17Helped
Use triplet rather than pairwise comparison learningJoint SIR-and-CIR model · CUHK03rank-1 accuracy (%): 43.36 -> 51.33Helped
Combine pairwise- and triplet-trained matching modelsJoint SIR-and-CIR model · CUHK03rank-1 accuracy (%): 51.33 -> 52.17Helped
Pretrain on CUHK03 before fine-tuningPairwise comparison model · CUHK01rank-1 accuracy (%): 58.93% -> 61.20%Helped
Pretrain on CUHK03 before fine-tuningTriplet comparison model · CUHK01rank-1 accuracy (%): 65.21% -> 71.30%Helped
Pretrain on CUHK03 before fine-tuningCombined comparison model · CUHK01rank-1 accuracy (%): 65.71% -> 71.80%Helped

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