Performance comparison of different methods on cross-domain person re-identification tasks (mAP, Rank-1, Rank-5, Rank-10).

<p>Performance comparison of different methods on cross-domain person re-identification tasks (mAP, Rank-1, Rank-5, Rank-10).</p>

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Bibliographic Details
Main Author:	Xuemei Bai (169120) (author)
Other Authors:	Yuqing Zhang (32031) (author), Chenjie Zhang (11328211) (author), Zhijun Wang (31218) (author)
Published:	2025
Subjects:	Neuroscience Space Science Biological Sciences not elsewhere classified mean average precision innovative loss function target domains makes scale public datasets effective technical support domain migration performance 9 %, respectively model extremely challenging limited model performance unsupervised domain adaptation label noise processing label generated noise xlink "> person contrast learning technique unsupervised domain adapted identification </ p existing uda methods unsupervised domain label noise target domain contrast learning public safety label refinement effective way domain difference model training two large training process thereby enhancing still significant probabilistic uncertainty paper provides paper proposes negative impact method experiments many applications labeled data intelligent surveillance identification task good solution generation process generalization ability first enhance feature space feature representation experimental validation 4 %, 1 accuracy
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