2 papers
cs.CV2026
Towards Universal Video MLLMs with Attribute-Structured and Quality-Verified Instructions
Yunheng Li, Hengrui Zhang, Meng-Hao Guo +5
Universal video understanding requires modeling fine-grained visual and audio information over time in diverse real-world scenarios. However, the performance of existing models is…
cs.CV2025
TempSamp-R1: Effective Temporal Sampling with Reinforcement Fine-Tuning for Video LLMs
Yunheng Li, Jing Cheng, Shaoyong Jia +4
This paper introduces TempSamp-R1, a new reinforcement fine-tuning framework designed to improve the effectiveness of adapting multimodal large language models (MLLMs) to video tem…