MIP-GAF: A MLLM-annotated Benchmark for Most Important Person Localization and Group Context Understanding
This work addresses the problem of social situation understanding for AI systems by providing a new benchmark, though it is incremental as it focuses on dataset creation rather than novel algorithms.
The paper tackles the challenge of Most Important Person (MIP) localization in social events by creating a large-scale 'in-the-wild' dataset annotated using a Multimodal Large Language Model (MLLM), and benchmarking shows a significant performance drop for state-of-the-art methods, indicating they are less robust in real-world scenarios.
Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To this end, we aim to address the problem by annotating a large-scale `in-the-wild' dataset for identifying human perceptions about the `Most Important Person (MIP)' in an image. The paper provides a thorough description of our proposed Multimodal Large Language Model (MLLM) based data annotation strategy, and a thorough data quality analysis. Further, we perform a comprehensive benchmarking of the proposed dataset utilizing state-of-the-art MIP localization methods, indicating a significant drop in performance compared to existing datasets. The performance drop shows that the existing MIP localization algorithms must be more robust with respect to `in-the-wild' situations. We believe the proposed dataset will play a vital role in building the next-generation social situation understanding methods. The code and data is available at https://github.com/surbhimadan92/MIP-GAF.