1 citations · 1 across the 4 of their papers we have counts for
9 papers · 1 filter
MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization
Gengyuan Liu, Nanzhou Wang, Chang Liu +5
Vision-language models exhibit remarkable zero-shot capabilities but suffer significant performance degradation under distribution shifts. While test-time adaptation (TTA) via Low-…
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…
SAMTok: Representing Any Mask with Two Words
Yikang Zhou, Tao Zhang, Dengxian Gong +13
Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-lev…
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…
Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs
Haochen Wang, Yuhao Wang, Tao Zhang +13
While Multimodal Large Language Models (MLLMs) excel at holistic understanding, they struggle in capturing the dense world with complex scenes, requiring fine-grained analysis of i…