3 papers
cs.CV2025
Towards Effective Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval
Tao Chen, Shaobo Ju, Qiong Wu +6
Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose an effective and efficient para…
cs.CV2025
CUS-GS: A Compact Unified Structured Gaussian Splatting Framework for Multimodal Scene Representation
Yuhang Ming, Chenxin Fang, Xingyuan Yu +4
Recent advances in Gaussian Splatting based 3D scene representation have shown two major trends: semantics-oriented approaches that focus on high-level understanding but lack expli…
cs.CV2025
Grounded Chain-of-Thought for Multimodal Large Language Models
Qiong Wu, Xiangcong Yang, Yiyi Zhou +4
Despite great progress, existing multimodal large language models (MLLMs) are prone to visual hallucination, greatly impeding their trustworthy applications. In this paper, we stud…