3 papers
cs.CV2026
SVAgent: Storyline-Guided Long Video Understanding via Cross-Modal Multi-Agent Collaboration
Zhongyu Yang, Zuhao Yang, Shuo Zhan +3
Video question answering (VideoQA) is a challenging task that requires integrating spatial, temporal, and semantic information to capture the complex dynamics of video sequences. A…
cs.CV2026
VideoZeroBench: Probing the Limits of Video MLLMs with Spatio-Temporal Evidence Verification
Jiahao Meng, Tan Yue, Qi Xu +7
Recent video multimodal large language models achieve impressive results across various benchmarks. However, current evaluations suffer from two critical limitations: (1) inflated…
cs.CV2025
Beyond Isolated Facts: Synthesizing Narrative and Grounded Supervision for VideoQA
Jianxin Liang, Tan Yue, Yuxuan Wang +4
The performance of Video Question Answering (VideoQA) models is fundamentally constrained by the nature of their supervision, which typically consists of isolated, factual question…