Papers by Quanjun Yin
MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression Comprehension (2024.emnlp-main)
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| Challenge: | Existing methods for Referring Expression Comprehension (REC) lack specific domain abilities for precise local visual perception and visual-language alignment. |
| Approach: | They propose a framework for Parameter-Efficient Transfer Learning to localize a visual region via natural language using a prior-guided prior. |
| Outcome: | The proposed framework achieves the best accuracy compared to the current methods with only 1.41% tunable backbone parameters. |
PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)
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Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin
| Challenge: | Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management. |
| Approach: | They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory. |
| Outcome: | The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models. |
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)
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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li
| Challenge: | Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces. |
| Approach: | They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings. |
| Outcome: | The citycube benchmark examines the performance of current vision-language models in urban environments. |
Generation and Extraction Combined Dialogue State Tracking with Hierarchical Ontology Integration (2021.emnlp-main)
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| Challenge: | Current models are not satisfactory for solving out-of-vocabulary problems . current models assume that the task ontology is well defined in advance . |
| Approach: | They propose to enhance the interrelation between slots with masked hierarchical attention. |
| Outcome: | The proposed model yields a significant performance gain over current state-of-the-art model and is more robust to out-ofvocabulary problem compared with other methods. |
Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution (2026.acl-long)
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| Challenge: | Large Language Model (LLM)-based optimization has shown promise for autonomous problem solving, but most approaches cast LLMs as passive constraint checkers rather than proactive strategy designers. |
| Approach: | They propose an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning. |
| Outcome: | Extensive experiments on three challenging COP benchmarks validate AutoCO’s consistent effectiveness and superior performance, especially in hard regimes where current methods degrade. |