1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
Post-Completion Learning for Language Models
Xiang Fei, Siqi Wang, Shu Wei +5
Current language model training paradigms typically terminate learning upon reaching the end-of-sequence (<eos>) token, overlooking the potential learning opportunities in the post…
MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement
Weitao Jia, Jinghui Lu, Haiyang Yu +17
Recent advances demonstrate that reinforcement learning with verifiable rewards (RLVR) significantly enhances the reasoning capabilities of large language models (LLMs). However, s…
Vision as LoRA
Han Wang, Yongjie Ye, Bingru Li +5
We introduce Vision as LoRA (VoRA), a novel paradigm for transforming an LLM into an MLLM. Unlike prevalent MLLM architectures that rely on external vision modules for vision encod…
Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLM
Han Wang, Yuxiang Nie, Yongjie Ye +6
The application of Large Vision-Language Models (LVLMs) for analyzing images and videos is an exciting and rapidly evolving field. In recent years, we've seen significant growth in…
Elysium: Exploring Object-level Perception in Videos via MLLM
Han Wang, Yanjie Wang, Yongjie Ye +2
Multi-modal Large Language Models (MLLMs) have demonstrated their ability to perceive objects in still images, but their application in video-related tasks, such as object tracking…