VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding
We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments sh...
| Authors | Lin Fu, Zheyuan Yang, Yang Wang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 3 June 2026 |
Connected in the knowledge graph
- minuzero/VideoKR-EvalDataset · cited by
- minuzero/VideoKR-TrainDataset · cited by
Models and datasets referencing this paper
- minuzero/VideoKR-TrainDataset
- minuzero/VideoKR-EvalDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.