2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
Lost in the Hype: Revealing and Dissecting the Performance Degradation of Medical Multimodal Large Language Models in Image Classification
Xun Zhu, Fanbin Mo, Xi Chen +6
The rise of multimodal large language models (MLLMs) has sparked an unprecedented wave of applications in the field of medical imaging analysis. However, as one of the earliest and…
BoxComm: Benchmarking Category-Aware Commentary Generation and Narration Rhythm in Boxing
Kaiwen Wang, Kaili Zheng, Rongrong Deng +3
Recent multimodal large language models (MLLMs) have shown strong capabilities in general video understanding, driving growing interest in automatic sports commentary generation. H…
Towards Metric-Aware Multi-Person Mesh Recovery by Jointly Optimizing Human Crowd in Camera Space
Kaiwen Wang, Kaili Zheng, Yiming Shi +2
Multi-person human mesh recovery from a single image is a challenging task, hindered by the scarcity of in-the-wild training data. Prevailing in-the-wild human mesh pseudo-ground-t…
Enhancing Multi-task Learning Capability of Medical Generalist Foundation Model via Image-centric Multi-annotation Data
Xun Zhu, Fanbin Mo, Zheng Zhang +6
The emergence of medical generalist foundation models has revolutionized conventional task-specific model development paradigms, aiming to better handle multiple tasks through join…
MedM-VL: What Makes a Good Medical LVLM?
Yiming Shi, Shaoshuai Yang, Xun Zhu +4
Medical image analysis is essential in modern healthcare. Deep learning has redirected research focus toward complex medical multimodal tasks, including report generation and visua…
Med-2E3: A 2D-Enhanced 3D Medical Multimodal Large Language Model
Yiming Shi, Xun Zhu, Kaiwen Wang +4
3D medical image analysis is essential for modern healthcare, yet traditional task-specific models are inadequate due to limited generalizability across diverse clinical scenarios.…