Can Person-Level Attributes Improve Group Re-Identification?

Group re-identification (G-ReID) attempts to recognize human groups across multiple camera perspectives. It is a challenging task due to occlusion, perspective variation, and illumination change. While Person Attribute Recognition (PAR) methods have shown robustness under similar challenges, yet their potential in G-ReID remains unexplored. Though existing G-ReID datasets are well-crafted, however, they lack person-level attribute annotations. This restricts G-ReID methods to explore attribute-based matching, essentially limiting their capability of multi-modal analysis. In this work, we bridge this gap by utilizing person-level attributes for group-level re-identification. We introduce PAG-ReID (Person Attribute based Group Re-identification), a large-scale dataset constructed by combining three popular G-ReID datasets: CM-Group, Road Group, and CUHK-SYSU-Group. PAG-ReID includes 19K group images encompassing 2,148 groups and 5,504 unique person IDs. At the person level, it provides 25K individual images, each annotated with 19 diverse human attributes, resulting in nearly 475K fine-grained annotations. Next, we propose an effective baseline (CLIP-based) that transforms attribute information into natural language descriptions, enabling joint multi-modal (visual-textual) reasoning for PAR as well as G-ReID tasks. Experiments demonstrate the effectiveness of our approach, setting a new direction for person attribute-centric group re-identification. To our knowledge, this is the first work to unify G-ReID and PAR in a single multi-modal framework. PAG-ReID can be found at https://github.com/draxler1/PAG-ReID.