4 papers
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding
Zhihong Zhang, Xiaojian Huang, Jin Xu +4
Multimodal reward models (MRMs) play a crucial role in the training, inference, and evaluation of Large Vision Language Models (LVLMs) by assessing response quality. However, exist…
ReasVQA: Advancing VideoQA with Imperfect Reasoning Process
Jianxin Liang, Xiaojun Meng, Huishuai Zhang +3
Video Question Answering (VideoQA) is a challenging task that requires understanding complex visual and temporal relationships within videos to answer questions accurately. In this…
CDS: Knowledge Component-Driven Data Synthesis Guided by Cognitive Diagnosis Theory
Haokun Zhao, Jinyi Han, Jiaqing Liang +3
Large Language Models (LLMs) have achieved significant advancements, but the increasing complexity of tasks and higher performance demands highlight the need for continuous improve…
VideoLLM Knows When to Speak: Enhancing Time-Sensitive Video Comprehension with Video-Text Duet Interaction Format
Yueqian Wang, Xiaojun Meng, Yuxuan Wang +4
Recent researches on video large language models (VideoLLM) predominantly focus on model architectures and training datasets, leaving the interaction format between the user and th…