40 citations · 87 across the 23 of their papers we have counts for
9 papers · 1 filter
From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding
Yuyuan Liu, Yiping Ji, Anjie Le +6
Finetuning Large Vision-Language Models with reinforcement learning has emerged as a promising approach to enhance their capability in object-level grounding. However, existing met…
PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation
Xin-Sheng Chen, Jiayu Zhu, Pei-lin Li +3
Slides serve as a critical medium for conveying information in presentation-oriented scenarios such as academia, education, and business. Despite their importance, creating high-qu…
MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows
Weixiang Shen, Chengzhi Shen, Yanzhu Hu +12
Medical imaging benchmarks often evaluate VLMs on pre-selected 2D images, slices, crops, or patches, making evaluation closer to visual recognition. Real clinical workflows impose…
MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
Jiazhen Pan, Che Liu, Junde Wu +6
Reasoning is a critical frontier for advancing medical image analysis, where transparency and trustworthiness play a central role in both clinician trust and regulatory approval. A…
Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation
Junde Wu, Jiayuan Zhu, Yunli Qi +4
We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large…
Medical SAM 2: Segment medical images as video via Segment Anything Model 2
Jiayuan Zhu, Abdullah Hamdi, Yunli Qi +2
Medical image segmentation plays a pivotal role in clinical diagnostics and treatment planning, yet existing models often face challenges in generalization and in handling both 2D…