{"type":"ScholarlyArticle","title":"Enhancing Cross-modal Completion and Alignment for Unsupervised Incomplete Text-to-Image Person Retrieval","authors":[{"name":"Tiantian Gong","slug":"tiantian-gong","given":"Tiantian","family":"Gong","affiliation":"Nanjing University of Aeronautics and Astronautics"},{"name":"Junsheng Wang","slug":"junsheng-wang","given":"Junsheng","family":"Wang","affiliation":"Nanjing University of Science and Technology"},{"name":"Liyan Zhang","slug":"liyan-zhang","given":"Liyan","family":"Zhang","affiliation":"Nanjing University of Aeronautics and Astronautics"}],"abstract":"Traditional text-image person retrieval methods heavily rely on fully matched and identity-annotated multimodal data, representing an ideal yet limited scenario. The issues of handling incomplete multimodal data and the complexities of labeling multimodal data are common challenges encountered in real-world applications. In response to these challenges encountered, we consider a more robust and pragmatic setting termed unsupervised incomplete text-image person retrieval, where person images and text descriptions are not fully matched and lack the supervision of identity labels. To tackle these two problems, we propose the Enhancing Cross-modal Completion and Alignment (ECCA) method. Specifically, we propose a feature-level cross-modal completion strategy for incomplete data. This approach leverages the available cross-modal high semantic similarity features to construct relational graphs for missing modal data, which can generate more reliable completion features.\r\nAdditionally, to address the cross-modal matching ambiguity, we propose weighted inter-instance granularity alignment as well as enhanced prototype-wise granularity alignment modules that can map semantically similar image-text pairs more compact in the common embedding space. Extensive experiments on public datasets, fully demonstrate the consistent superiority of our method over SOTA text-image person retrieval methods.","doi":"10.24963/ijcai.2024/88","url":"https://ijcai.org/proceedings/2024/88","pdf":"https://ijcai.org/proceedings/2024/0088.pdf","markdown":"https://ijcai.org/proceedings/2024/88.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/88","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":88,"pages":{"first":794,"last":802},"keywords":["Computer Vision: CV: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"Methods ID Easy Setting Medium Setting Hard Setting Rank-1 Rank-5 Rank-10 Rank-1 Rank-5 Rank-10 Rank-1 Rank-5 Rank-10 CMPM/C[Zhang and Lu, 2018] 40.79 63.01 74.58 39.82 62.54 74.84 26.96 52.48 63.88 MIA [Niu et al., 2020] 46.23 68.56 77.64 43.64 66.37 74.11 29.78 54.94 65.71 SCAN [Lee et al., 2018] 49.84 71.96 79.38 46.87 69.43 77.64 31.83 53.77 64.93 ViTAA [Wang et al., 2020] 49.32 70.67 79.51 47.24 69.56 78.79 31.48 54.96 65.02 SSAN [Ding et al., 2021] 53.41 74.34 82.31 49.05 71.76 79.73 34.04 57.74 68.35 AXM-Net [Farooq et al., 2022] 57.28 77.18 84.11 53.23 74.24 81.97 36.64 59.98 69.74 LGUR [Shao et al., 2022] 58.77 78.36 85.41 53.95 74.79 81.77 35.61 59.36 69.18 IRRA [Jiang and Ye, 2023] 63.80 83.28 89.27 59.09 79.50 86.59 50.42 73.76 81.61 ECCA 65.23 85.14 91.29 63.70 83.11 89.84 56.38 77.24 85.07 Table 1: Performance comparisons under three different settings on the CUHK-PEDES benchmark dataset. Methods ID Easy Setting Medium Setting Hard Setting Rank-1 Rank-5 Rank-10 Rank-1 Rank-5 Rank-10 Rank-1 Rank-5 Rank-10 CMPM/C [Zhang and Lu, 2018] 34.69 55.86 65.71 29.73 51.17 60.84 15.62 30.26 41.59 MIA [Niu et al., 2020] 39.13 60.34 69.16 36.17 58.52 67.83 19.54 35.44 45.95 SCAN [Lee et al., 2018] 42.53 65.41 71.82 41.37 63.95 71.89 22.24 40.27 51.86 ViTAA [Wang et al., 2020] 43.79 65.86 73.42 42.78 64.55 72.41 22.61 40.62 51.37 SSAN [Ding et al., 2021] 46.27 67.06 75.52 44.86 66.72 74.58 24.83 42.64 53.19 AXM-Net [Farooq et al., 2022] 50.31 70.62 77.93 48.26 67.14 76.69 28.26 49.53 59.49 LGUR [Shao et al., 2022] 52.73 70.55 78.41 48.32 68.73 76.91 29.73 51.26 60.19 IRRA [Jiang and Ye, 2023] 51.65 71.66 78.98 47.49 68.56 76.48 37.61 59.22 68.09 ECCA 54.18 74.34 81.10 51.17 71.04 78.32 42.08 62.95 73.16 Table 2: Performance comparisons under three different settings on the ICFG-PEDES benchmark dataset. 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