Slice-and-Align for Clothes-Irrelevant Features: A Clothes-Changing Person Re-Identification Approach Without Additional Input

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 Slice-and-Align Framework (SA), 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.