Papers by Guosheng Zhao
Normal-Abnormal Decoupling Memory for Medical Report Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for capturing nuanced visual information are prone to data bias and noise. |
| Approach: | They propose a normal-abnormal semantic decoupling network that utilizes abnormal pattern memory to optimize visual extraction through the extraction of abnormal semantics from the reports. |
| Outcome: | The proposed approach surpasses the current state-of-the-art methods on the benchmark MIMIC-CXR and shows excellent performance on the same dataset. |
Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026.acl-long)
Copied to clipboard
Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) is a key parameter-efficient fine-tuning method . however, its effectiveness is hampered by semantic drift and structural incoherence . |
| Approach: | They propose a low-rank Adaptation framework that tackles semantic drift and structural incoherence by pruning task-irrelevant directions. |
| Outcome: | Experiments on large language models, vision models, and vision models show that the proposed framework outperforms LoRA and advanced dynamic rank allocation and sparsity-based methods. |