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arXiv Computer Vision By Zhiyun Jiang, Hanyong Wang, Binbin Liang, Yu Xie, Menglong Yang, Wei Li

Benchmarking MLLMs via Cognitive Expected Scene Graph for Safety-Critical Visual Negation Understanding

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The paper introduces a new benchmark for evaluating Multi‑Modal Large Language Models (MLLMs) on visual negation understanding, focusing on safety-critical scenarios. It defines the Scene Negation Understanding under Safety Cognition (SNUS) task and presents a high‑fidelity negative caption dataset that maps dense assertions of localized hazards. The authors also propose the Cognitive Expected Scene Graph (CESG) Score, a polarity‑aware, structure‑grounded metric that remains robust under semantic reversals, revealing that existing models and traditional metrics fail on this task.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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