Visible-Infrared Cross-Modal Person Re-identification based on Positive Feedback

Visible-infrared person re-identification (VI-ReID) is undoubtedly a challenging cross-modality person retrieval task with increasing appreciation. Compared to traditional person ReID that focuses on person images in a single RGB mode, VI-ReID suffers from additional cross-modality discrepancy due to the different imaging processes of spectrum cameras. Several effective attempts have been made in recent years to narrow cross-modality gap aiming to improve the re-identification performance, but rarely study the key problem of optimizing the search results combined with relevant feedback. In this paper, we present the idea of cross-modality visible-infrared person re-identification combined with human positive feedback. This method allows the user to quickly optimize the search performance by selecting strong positive samples during the re-identification process. We have validated the effectiveness of our method on a public dataset, SYSU-MM01, and results confirmed that the proposed method achieved superior performance compared to the current state-of-the-art methods.