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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.04384 |
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| _version_ | 1866911251604439040 |
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| author | Safwan, Itbaan Shaikh, Muhammad Annas Haaris, Muhammad Khan, Ramail Tahir, Muhammad Atif |
| author_facet | Safwan, Itbaan Shaikh, Muhammad Annas Haaris, Muhammad Khan, Ramail Tahir, Muhammad Atif |
| contents | We present a multi-task framework for the MediaEval Medico 2025 challenge, leveraging a LoRA-tuned Florence-2 model for simultaneous visual question answering (VQA), explanation generation, and visual grounding. The proposed system integrates three curated datasets: (1) Kvasir-VQA-x1 for question-answer learning, (2) a synthetically enriched explanation dataset offering structured medical reasoning, and (3) text-to-region pairs linking visual features with segmentation masks. This multi-task setup enables the model to jointly learn visual grounding, reasoning, and interpretation, producing responses that are both accurate and interpretable. Extensive evaluation demonstrates that our approach substantially improves over single-task baselines in both answer accuracy and visual localization, highlighting the effectiveness of grounded multi-task learning for medical VQA applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Task Learning for Visually Grounded Reasoning in Gastrointestinal VQA Safwan, Itbaan Shaikh, Muhammad Annas Haaris, Muhammad Khan, Ramail Tahir, Muhammad Atif Computer Vision and Pattern Recognition Machine Learning We present a multi-task framework for the MediaEval Medico 2025 challenge, leveraging a LoRA-tuned Florence-2 model for simultaneous visual question answering (VQA), explanation generation, and visual grounding. The proposed system integrates three curated datasets: (1) Kvasir-VQA-x1 for question-answer learning, (2) a synthetically enriched explanation dataset offering structured medical reasoning, and (3) text-to-region pairs linking visual features with segmentation masks. This multi-task setup enables the model to jointly learn visual grounding, reasoning, and interpretation, producing responses that are both accurate and interpretable. Extensive evaluation demonstrates that our approach substantially improves over single-task baselines in both answer accuracy and visual localization, highlighting the effectiveness of grounded multi-task learning for medical VQA applications. |
| title | Multi-Task Learning for Visually Grounded Reasoning in Gastrointestinal VQA |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2511.04384 |