Papers by Jihang Jin
MedKInstruct: A Multimodal Knowledge Graph Based Framework for Multi-Hop and Hard-Negative Instruction Data Synthesis in MedVQA (2026.findings-acl)
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| Challenge: | Existing methods for medical visual question answering focus on image–caption pairs, limiting the model’s ability to learn relevant medical knowledge during training. |
| Approach: | They propose to synthesize instruction data from image–caption pairs and incorporate a multimodal medical knowledge graph to assist LVLMs in synthesizing knowledge-intensive instruction data. |
| Outcome: | The proposed model outperforms existing methods on the public datasets Slake and VQA-RAD by 4.16% and 4.50%. |
Tiny Scales, Great Challenges: The Limits of Multimodal LLMs in Scale Recognition (2026.acl-long)
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| Challenge: | Existing benchmarks focus on a single type of quantity or a specific format, lacking a comprehensive evaluation of scale recognition capabilities. |
| Approach: | They propose a visual scale recognition benchmark built using images from COCO, Open Images, and Flickr to evaluate scale recognition capabilities of multimodal large language models. |
| Outcome: | The proposed model achieves 42.60% accuracy, lower than the 97.40% of humans. |