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Slice-and-Align for Clothes-Irrelevant Features: A Clothes-Changing Person Re-Identification Approach Without Additional Input
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DOI：10.1109/TMM.2026.3651031.png)
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Abstract
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Clothes-changing person re-identification (CC Re-ID) focuses on recognizing pedestrians in a long-term with changes in clothes. Prior arts extract clothes-irrelevant features either by introducing extra modality or clothing labels, having their respective limitations. Instead, we seek to extract clothes-irrelevant features without additional input. We first analyze and find that one impediment to extracting clothes-irrelevant features is the co-occurrence of samples with the same clothes and the same identity. Inspired by this observation, we propose a novel CC Re-ID approach using no additional input. We introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i>lice-and-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i>lign Framework (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</i>), which employs a straightforward and intuitive prior: the upper and lower clothes of a person are usually different. SA is a dual-stream framework that slices the original image into upper and lower halves, and then aligns them to extract clothes-irrelevant features. On image CC Re-ID datasets, SA outperforms methods without additional input by a large margin and is comparable to or even better than methods with additional input. Besides, SA also outperforms state-of-the-art on video CC Re-ID task.
Keywords:
Person re-identification
feature alignment
clothes-changing person re-identification
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