Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification

TL;DR

This paper introduces a semantic-guided pixel sampling method for cloth-changing person re-identification, enabling models to learn cloth-irrelevant cues and improve identification accuracy despite clothing changes.

Contribution

It proposes a novel pixel sampling approach that automatically learns cloth-irrelevant features, enhancing re-ID performance under clothing variations.

Findings

Achieved 65.8% Rank1 accuracy on PRCC dataset.

Outperformed previous methods significantly.

Demonstrated robustness to clothing changes.

Abstract

Cloth-changing person re-identification (re-ID) is a new rising research topic that aims at retrieving pedestrians whose clothes are changed. This task is quite challenging and has not been fully studied to date. Current works mainly focus on body shape or contour sketch, but they are not robust enough due to view and posture variations. The key to this task is to exploit cloth-irrelevant cues. This paper proposes a semantic-guided pixel sampling approach for the cloth-changing person re-ID task. We do not explicitly define which feature to extract but force the model to automatically learn cloth-irrelevant cues. Specifically, we first recognize the pedestrian's upper clothes and pants, then randomly change them by sampling pixels from other pedestrians. The changed samples retain the identity labels but exchange the pixels of clothes or pants among different pedestrians. Besides, we adopt a loss function to constrain the learned features to keep consistent before and after changes. In this way, the model is forced to learn cues that are irrelevant to upper clothes and pants. We conduct extensive experiments on the latest released PRCC dataset. Our method achieved 65.8% on Rank1 accuracy, which outperforms previous methods with a large margin. The code is available at https://github.com/shuxjweb/pixel_sampling.git.