Video-Oasis: Rethinking Evaluation of Video Understanding

2026-03-31

AI Summary

Existing video understanding benchmarks struggle to disentangle whether performance gains stem from visual perception, linguistic reasoning, or knowledge priors, often overlooking spatiotemporal understanding—a core requirement for genuine video comprehension. To address this, this work introduces Video-Oasis, a diagnostic suite that employs systematic benchmark analysis, sample-level ablation, and spatiotemporal-semantic disentanglement. It reveals, for the first time, that 54% of samples in mainstream benchmarks can be answered correctly without relying on visual or temporal cues. Moreover, state-of-the-art models perform only marginally above random guessing on the subset of samples that genuinely require spatiotemporal reasoning. This study not only exposes significant biases in current evaluation protocols but also establishes reproducible evaluation standards and design principles for developing robust video understanding models and more reliable benchmarks.

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.

Problem

• video understanding
• evaluation benchmark
• spatio-temporal reasoning
• model diagnostics
• visual perception

Innovation

• video understanding
• evaluation benchmark
• spatio-temporal reasoning
• diagnostic suite
• model robustness