Papers by Yuyi Zhang
Draft, Verify, Restore: Self-Refining Historical Inscription Restoration with a Unified MLLM (2026.acl-long)
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| Challenge: | Existing methods for end-to-end historical inscription restoration rely on task-separated pipelines with irreversible error accumulation and patch-based generation that sacrifices page-level consistency. |
| Approach: | They propose a unified MLLM for end-to-end historical inscription restoration that integrates draft-guided localization and Hierarchical self-refinement to enable accurate damage localization. |
| Outcome: | The proposed model achieves superior performance in both text restoration accuracy and appearance restoration quality. |
Reviving Cultural Heritage: A Novel Approach for Comprehensive Historical Document Restoration (2025.acl-long)
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Yuyi Zhang, Peirong Zhang, Zhenhua Yang, Pengyu Yan, Yongxin Shi, Pengwei Liu, Fengjun Guo, Lianwen Jin
| Challenge: | Existing methods for historical document restoration focus on single modality or limited-size restoration, failing to meet practical needs. |
| Approach: | They propose a full-page HDR dataset and an automated HDR solution to replace manual restoration methods. |
| Outcome: | The proposed solution improves OCR accuracy from 46.83% to 84.05% when processing severely damaged documents, with enhancement to 94.25% through human-machine collaboration. |
Doc-V*: Coarse-to-Fine Interactive Visual Reasoning for Multi-Page Document VQA (2026.acl-long)
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Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang, Yuyi Zhang, Wenyu Ruan, Xiaojin Zhang, Zhongyu Wei, Zhenbo Luo, Jian Luan, Wei Chen, Xiang Bai
| Challenge: | Existing OCR-free approaches to document visual question answering are brittle and passive. |
| Approach: | They propose an OCR-free agentic framework that casts multi-page DocVQA as sequential evidence aggregation. |
| Outcome: | The proposed framework outperforms open-source and proprietary models in five benchmarks and improves out-of-domain performance by 47.9% over baseline. |
DPGA-TextSyn: Differentially Private Genetic Algorithm for Synthetic Text Generation (2025.findings-acl)
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Zhonghao Sun, Zhiliang Tian, Yiping Song, Yuyi Si, Juhua Zhang, Minlie Huang, Kai Lu, Zeyu Xiong, Xinwang Liu, Dongsheng Li
| Challenge: | Existing methods to fine-tune large language models pose privacy risks . researchers have synthesized data with strong generation capabilities closed-source LLMs to alleviate this problem . |
| Approach: | They propose to combine general LLMs with genetic algorithm to produce relevant and diverse synthetic text under differential privacy constraints. |
| Outcome: | The proposed method significantly improves the performance of the model in downstream tasks while ensuring privacy. |