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Author: search.filters.author.Angsarawanee, ThanatwitStart date: 2024

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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.
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    Generating Pseudo-labels for Car Damage Segmentation Using Deep Spectral Method
    (2024-01-01)
    Taspan, Nonthapaht
    ;
    Madthing, Bukorree
    ;
    Chetprayoon, Panumate
    ;
    Angsarawanee, Thanatwit
    ;
    Pasupa, Kitsuchart
    Car damage segmentation, an integral part of vehicle damage assessment, involves identifying and classifying various types of damages from images of vehicles, thereby enhancing the efficiency and accuracy of assessment processes. This paper introduces an efficient approach for car damage assessment by combining pseudo-labeling and deep learning techniques. The method addresses the challenge of limited labeled data in car damage segmentation by leveraging unlabeled data. Pseudo-labels are generated using a deep spectral approach and refined through merge and flip-bit operations. Two models, i.e., Mask R-CNN and SegFormer, are trained using a combination of ground truth labels and pseudo-labels. Experimental evaluation of the CarDD dataset demonstrates the superior accuracy of our method, achieving improvements of 12.9% in instance segmentation and 18.8% in semantic segmentation when utilizing a 1/2 ground truth ratio. In addition to enhanced accuracy, our approach offers several benefits, including time savings, cost reductions, and the elimination of biases associated with human judgment. By enabling more precise and reliable identification of car damages, our method enhances the overall effectiveness of the assessment process. The integration of pseudo-labeling and deep learning techniques in car damage assessment holds significant potential for improving efficiency and accuracy in real-world scenarios.