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arXiv · 2505.07704

Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images

Elisei Rykov·Kseniia Petrushina·Kseniia Titova·Anton Razzhigaev·Alexander Panchenko·Vasily Konovalov
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Abstract

Measuring how real images look is a complex task in artificial intelligence research. For example, an image of a boy with a vacuum cleaner in a desert violates common sense. We introduce a novel method, which we call Through the Looking Glass (TLG), to assess image common sense consistency using Large Vision-Language Models (LVLMs) and Transformer-based encoder. By leveraging LVLMs to extract atomic facts from these images, we obtain a mix of accurate facts. We proceed by fine-tuning a compact attention-pooling classifier over encoded atomic facts. Our TLG has achieved a new state-of-the-art performance on the WHOOPS! and WEIRD datasets while leveraging a compact fine-tuning component.

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Elisei Rykov, Kseniia Petrushina, Kseniia Titova, Anton Razzhigaev, Alexander Panchenko, Vasily Konovalov. 2025-05-12. Through the Looking Glass: Common Sense Consistency Evaluation of Weird Images. https://arxiv.org/abs/2505.07704

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