Papers by Shuai Niu
Knowledge-Augmented Multimodal Clinical Rationale Generation for Disease Diagnosis with Small Language Models (2025.acl-long)
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| Challenge: | Existing models struggle to balance predictive accuracy with human-understandable rationales. |
| Approach: | They propose to enhance LLMs by leveraging rationale distillation and domain knowledge injection for trustworthy multimodal rationale generation. |
| Outcome: | Experiments on real-world medical datasets show that ClinRaGen achieves state-of-the-art performance in disease diagnosis and rationale generation. |
Improving Deep Embedded Clustering via Learning Cluster-level Representations (2022.coling-1)
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| Challenge: | Existing efforts to learn meaningful representations at the instance level are limited. |
| Approach: | They propose a deep embedded clustering model with cluster-level representation learning to jointly learn cluster and instance level representations. |
| Outcome: | The proposed model produces meaningful clusters on real-world short text datasets. |
AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents (2025.findings-acl)
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Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren, Hongsheng Li
| Challenge: | a new dataset is being developed to improve the capabilities of mobile GUI-control agents. |
| Approach: | They propose a dataset designed for generalist mobile GUI-control agents . they use screenshots from popular mobile applications to create a detailed GUI-annotated dataset . |
| Outcome: | The Android Multi-annotation EXpo (AMEX) is a large-scale dataset for generalist mobile GUI-control agents . it includes screenshots from popular mobile applications, which are annotated at multiple levels . |
ProMedTS: A Self-Supervised, Prompt-Guided Multimodal Approach for Integrating Medical Text and Time Series (2025.findings-acl)
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Shuai Niu, Jing Ma, Hongzhan Lin, Liang Bai, Zhihua Wang, V. W., Richard Yi Da Xu, Guo Li, Xian Yang
| Challenge: | Large language models excel at processing unstructured data, but integrating time series data with text remains a challenge. |
| Approach: | They propose a self-supervised multimodal framework that uses prompt-guided learning to unify heterogeneous data types. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on disease diagnosis tasks using real-world datasets. |