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cs.CV2026
VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
Lin Fu, Zheyuan Yang, Yang Wang +3
We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reas…
cs.CV2025
TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
Ziyao Shangguan, Chuhan Li, Yuxuan Ding +4
Existing benchmarks often highlight the remarkable performance achieved by state-of-the-art Multimodal Foundation Models (MFMs) in leveraging temporal context for video understandi…
cs.CV2025
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Yilun Zhao, Lujing Xie, Haowei Zhang +16
We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions…