Identifying the Most Important Person (MIP) in complex social and sports events remains a challenging problem due to the dynamic nature of group interactions, subtle visual cues, and context-dependent semantics. Traditional methods often struggle to accurately capture the interplay between individuals and the overarching activity, especially in unstructured real-world environments. In addition, the lack of strong supervision and the need for a deeper contextual understanding further complicate the task. In this work, we propose IMPACT, a novel multi-modal framework that leverages recent advances in vision language models to bridge the gap between visual perception and semantic reasoning. Our approach integrates structured scene understanding, natural language generation, and cross-modal learning to jointly model activity recognition and MIP localization. The method integrates language, vision, and spatial reasoning to improve scene interpretability as well as accuracy in group activity recognition tasks. By incorporating language-based representations, the proposed method enables interpretable and robust performance in sports-centric group activity scenarios. Comprehensive experiments on C-Sports and NCAA datasets demonstrate that the framework significantly enhances the localization of key individuals as well as the accuracy of activity prediction, laying the groundwork for a holistic scene understanding in human-centric video and image analysis. Our proposed method achieves an accuracy of 81.6% when compared with human annotator markings and an increase in mAP scores by ∼ 5% for MIP 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.

Pedestrian Attribute Recognition (PAR) serves as a fun-damental task in computer vision and is crucial for upgradign security systems. It helps in precisely identifying and characterizing various attributes of pedestrians. However, current PAR datasets have certain issues in representing a wide range of attributes correctly, which makes the ex-isting PAR methods less effective in real-world scenarios. Addressing this limitation, this paper introduces PEARL, a comprehensive dataset comprising of diverse pedestrian images annotated with 146 attributes. These samples have been sourced from surveillance videos across twelve coun-tries. This paper also formulates an image-based PAR using language-image fusion strategy and utilizes CLIP as a new evaluation baseline. Specifically, we leverage textual infor-mation by transforming sets of attributes into meaningful sentences. Addressing the inherent data imbalance in PAR, we provide three types of prompt settings to optimize the training of the CLIP model. Our evaluation encompasses a thorough assessment of the proposed baseline model across various datasets, including PEARL dataset as well as estab-lished PAR benchmarks such as PA100K, RAP, and PETA.