计算机工程与应用2024，Vol.60Issue(15)：122-132,11.DOI:10.3778/j.issn.1002-8331.2304-0332
高低显著性互补特征引导的跨模态行人重识别
Cross-Modal Pedestrian Re-Identification Guided by Complementary High and Low Salient Features
摘要
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 informa-tion 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 dis-criminative 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.关键词
跨模态/互补特征/次关键线索/特征融合/重识别Key words
cross-modal/complementary feature/sub-critical cues/feature fusion/re-identification(ReID)分类
信息技术与安全科学引用本文复制引用
陈明,郭立君,张荣..高低显著性互补特征引导的跨模态行人重识别[J].计算机工程与应用,2024,60(15):122-132,11.基金项目
浙江省自然科学基金/公益技术项目(LGF21F020008) （LGF21F020008）
宁波市公益性科技计划项目(2022S134). （2022S134）
