MAENet: Boosting Feature Representation for Cross-Modal Person Re-Identification with Pairwise Supervision

Abstract

Person re-identification aims at successfully retrieving the images of a specific person in the gallery dataset given a probe image. Among all the existing research areas related to person re-identification, visible to thermal person re-identification (VT-REID) has gained proliferating momentum. VT-REID is deemed to be a rather challenging task owing to the large cross-modality gap [25], cross-modality variation and intra-modality variation. Existing techniques generally tackle this problem by embedding cross-modality data with convolutional neural networks into shared feature space to bridge the cross-modality discrepancy, and subsequently, devise hinge losses on similarity learning to alleviate the variation. However, feature extraction methods based simply on convolutional neural networks may fail to capture the distinctive and modality-invariant features, resulting in noises for further re-identification techniques. In this work, we present a novel modality and appearance invariant embedding learning framework equipped with maximum likelihood learning to perform cross-modal person re-identification. Extensive and comprehensive experiments are conducted to test the effectiveness of our framework. Results demonstrated that the proposed framework yields state-of-the-art Re-ID accuracy on RegDB and SYSU-MM01 datasets.