7 citations · 11 across the 3 of their papers we have counts for
4 papers
EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis
Shengyuan Liu, Boyun Zheng, Wenting Chen +5
Endoscopic procedures are essential for diagnosing and treating internal diseases, and multi-modal large language models (MLLMs) are increasingly applied to assist in endoscopy ana…
Polyp-Gen: Realistic and Diverse Polyp Image Generation for Endoscopic Dataset Expansion
Shengyuan Liu, Zhen Chen, Qiushi Yang +4
Automated diagnostic systems (ADS) have shown significant potential in the early detection of polyps during endoscopic examinations, thereby reducing the incidence of colorectal ca…
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy
Te Yang, Jian Jia, Xiangyu Zhu +9
Large Language Models (LLMs) have strong instruction-following capability to interpret and execute tasks as directed by human commands. Multimodal Large Language Models (MLLMs) hav…
Training-free Subject-Enhanced Attention Guidance for Compositional Text-to-image Generation
Shengyuan Liu, Bo Wang, Ye Ma +6
Existing subject-driven text-to-image generation models suffer from tedious fine-tuning steps and struggle to maintain both text-image alignment and subject fidelity. For generatin…