1 citations · 1 across the 2 of their papers we have counts for
5 papers
Fusing Pixels and Genes: Spatially-Aware Learning in Computational Pathology
Minghao Han, Dingkang Yang, Linhao Qu +5
Recent years have witnessed remarkable progress in multimodal learning within computational pathology. Existing models primarily rely on vision and language modalities; however, la…
ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering
Yuxiang Nie, Han Wang, Yongjie Ye +15
This paper introduces ChineseVideoBench, a pioneering benchmark specifically designed for evaluating Multimodal Large Language Models (MLLMs) in Chinese Video Question Answering. T…
SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
Fangxun Shu, Yongjie Ye, Yue Liao +6
We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them wh…
SAIL-VL2 Technical Report
Weijie Yin, Yongjie Ye, Fangxun Shu +11
We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves…
SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement
Weijie Yin, Dingkang Yang, Hongyuan Dong +5
Vision Transformers (ViTs) are essential as foundation backbones in establishing the visual comprehension capabilities of Multimodal Large Language Models (MLLMs). Although most Vi…