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Author: search.filters.author.Angsarawanee, ThanatwitSubject: search.filters.subject.Multimodal Learning

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    Item type:Publication,
    Unified Multimodal-Multitask Learning for Vehicle Damage Assessment in Insurance Applications
    (2026-01-01)
    Phuengpanyaloet, Wongsapat
    ;
    Pasupa, Kitsuchart
    ;
    Angsarawanee, Thanatwit
    ;
    Chetprayoon, Panumate
    ;
    Sakdejayont, Theerat
    Automated vehicle damage assessment requires both precise localization and clear textual reporting. While existing methods typically treat these as separate tasks, the trade-offs of unified multimodal-multitask learning in this domain remain underexplored. This paper conducts a comparative study between a unified vision-language framework, Generative Region-to-Text Transformer (GRiT), and single-task baselines derived from GRiT by isolating the detection and captioning components. We adapt GRiT to the insurance domain using a dataset enriched with vehicle part annotations and structured damage descriptions. Experimental results demonstrate that the unified model achieves competitive detection performance (F<inf>1</inf>-score: 0.54), slightly outperforming the detection baseline model. Crucially, it significantly surpasses the caption baseline model in description quality (METEOR: 0.75, ROUGE: 0.70, BLEU: 0.46), confirming that object-level visual grounding is essential for accurate reporting. These findings indicate that unified multimodal learning enhances semantic interpretation without compromising localization accuracy, offering a promising direction for automated insurance workflows.