关键词: 跨模态, 互补特征, 次关键线索, 特征融合, 重识别

Abstract: Efficiently extracting salient information from pedestrian images and mitigating the modality discrepancy are crucial for improving the performance of cross-modal person re-identification (VI-ReID) tasks. Current approaches mainly utilize attention-based methods to enhance the learning of discriminative features on the pedestrians’ bodies. However, these methods only focus on the most salient regions of pedestrians, neglecting the complementary secondary cues present in the pedestrian images. Therefore, this paper proposes a saliency complementary feature guided network (SCFG-Net). Firstly, a complementary feature salient mining (CFSM) module is designed to infer salient features with global information from pedestrian images, as well as the secondary cues that are overlooked by attention mechanisms. These features are then fused to enhance the richness and discriminability of pedestrian image features. Additionally, a cross-modal discriminative feature fusion (CDFF) module is designed to alleviate the color discrepancy between modalities. Experimental results demonstrate the effectiveness of the proposed method on two publicly available datasets. In the single-shot mode of the SYSU-MM01 dataset, the proposed method achieves Rank-1 and mAP scores of 74.4% and 70.8%, respectively.

Key words: cross-modal, complementary feature, sub-critical cues, feature fusion, re-identification (ReID)