Papers by Zhenyue Qin
Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey (2026.findings-eacl)
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| Challenge: | Plane geometry problem solving has gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. |
| Approach: | They present a systematic review of existing work in PGPS and summarize their results. |
| Outcome: | The proposed frameworks are compared with existing frameworks and analyze them according to their architectural designs. |
Visual Prompting in LLMs for Enhancing Emotion Recognition (2024.emnlp-main)
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Qixuan Zhang, Zhifeng Wang, Dylan Zhang, Wenjia Niu, Sabrina Caldwell, Tom Gedeon, Yang Liu, Zhenyue Qin
| Challenge: | Existing methods for enhancing in-context emotion classification fail to include spatial relationships between different people and facial features within a single face. |
| Approach: | They propose a set-of-vision prompting approach that uses spatial information to mark targets precisely. |
| Outcome: | The proposed approach improves face count and emotion categorization while preserving the enriched image context. |
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models (2025.findings-naacl)
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Zhenyue Qin, Yu Yin, Dylan Campbell, Xuansheng Wu, Ke Zou, Ninghao Liu, Yih Chung Tham, Xiuzhen Zhang, Qingyu Chen
| Challenge: | Existing benchmarks for large vision-language models (LVLMs) are limited to ophthalmology-specific applications. |
| Approach: | They introduce a large-scale multimodal ophthalmology benchmark consisting of 21,993 instances across five ocular imaging modalities and 13 state-of-the-art LVLM representatives from closed-source, open-source and medical domains. |
| Outcome: | The proposed model shows significant performance drop in ophthalmology compared to other domains. |
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder (2025.findings-emnlp)
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| Challenge: | GeoDANO is a geometric vision-language model with a domain-agnostic vision encoder . it is currently limited to recognizing geometric features in general-purpose VLMs . |
| Approach: | They propose a geometric vision-language model with a domain-agnostic vision encoder for plane geometry problems. |
| Outcome: | The proposed model outperforms vision encoders in recognizing geometric features . it outperformed specialized methods for plane geometry problems and GPT-4o on MathVerse . |