Video-Oasis: Rethinking Evaluation of Video Understanding

arXiv cs.CV / 4/1/2026

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Key Points

The paper introduces Video-Oasis, a “sustainable diagnostic suite” aimed at re-evaluating how current video-understanding benchmarks measure spatio-temporal reasoning.
Their analysis finds that 54% of existing benchmark samples can be solved without visual input or temporal context, suggesting substantial benchmark contamination.
For the remaining samples, state-of-the-art models reportedly perform only slightly above random guessing, indicating that current evaluations may not reflect true video understanding.
Video-Oasis distills the key spatio-temporal challenges underlying video understanding and investigates which algorithmic design choices drive more robust performance.
The authors provide practical guidelines for future benchmark construction and for more rigorous architecture evaluation, with code released on GitHub.

Abstract

The inherent complexity of video understanding makes it difficult to attribute whether performance gains stem from visual perception, linguistic reasoning, or knowledge priors. While many benchmarks have emerged to assess high-level reasoning, the essential criteria that constitute video understanding remain largely overlooked. Instead of introducing yet another benchmark, we take a step back to re-examine the current landscape of video understanding. In this work, we provide Video-Oasis, a sustainable diagnostic suite designed to systematically evaluate existing evaluations and distill spatio-temporal challenges for video understanding. Our analysis reveals two critical findings: (1) 54% of existing benchmark samples are solvable without visual input or temporal context, and (2) on the remaining samples, state-of-the-art models exhibit performance barely exceeding random guessing. To bridge this gap, we investigate which algorithmic design choices contribute to robust video understanding, providing practical guidelines for future research. We hope our work serves as a standard guideline for benchmark construction and the rigorous evaluation of architecture development. Code is available at https://github.com/sejong-rcv/Video-Oasis.