{"type":"ScholarlyArticle","title":"Richer Semantics, Better Alignment: Aligning Visual Features with Explicit and Enriched Semantics for Visible-Infrared Person Re-Identification","authors":[{"name":"Neng Dong","slug":"neng-dong","given":"Neng","family":"Dong","affiliation":""},{"name":"Shuanglin Yan","slug":"shuanglin-yan","given":"Shuanglin","family":"Yan","affiliation":""},{"name":"Liyan Zhang","slug":"liyan-zhang","given":"Liyan","family":"Zhang","affiliation":""},{"name":"Jinhui Tang","slug":"jinhui-tang","given":"Jinhui","family":"Tang","affiliation":""}],"abstract":"Visible-infrared person re-identification (VIReID) retrieves pedestrian images with the same identity across different modalities. Existing methods learn visual features solely from images, failing to align them into the modality-invariant semantic space. In this paper, we propose a novel framework, termed Richer Semantics, Better Alignment (RSBA), to align visual features with explicit and enriched semantics. Specifically, we first develop an Explicit Semantics-Guided Feature Alignment (ESFA) module, which supplements textual descriptions for cross-modality images and aligns image-text pairs within each modality, alleviating the distribution discrepancy of visual features. We then devise a Consistent Similarity-Guided Indirect Alignment (CSIA) module, which constrains the similarity between intra-modality image-text pairs to be consistent with that between inter-modality text-text pairs, indirectly aligning visual features with cross-modality semantics. Furthermore, we design a Cross-View Semantics Compensation (CVSC) module, which integrates multi-view texts and improves the image-text matching of one-to-one in ESFA and CSIA to one-to-many, further strengthening the alignment of visual features within the semantic space. Extensive experimental results on three public datasets demonstrate the effectiveness and superiority of our proposed RSBA.","doi":"10.24963/ijcai.2025/104","url":"https://ijcai.org/proceedings/2025/104","pdf":"https://ijcai.org/proceedings/2025/0104.pdf","markdown":"https://ijcai.org/proceedings/2025/104.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/104","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":104,"pages":{"first":927,"last":935},"keywords":["Computer Vision: CV: Biomedical image analysis","Computer Vision: CV: Biometrics, face, gesture and pose recognition","Computer Vision: CV: Image and video retrieval","Computer Vision: CV: Machine learning for vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}