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arXiv · 2507.05799

Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reports

Amane Watahiki·Tomoki Doi·Taiga Shinozaki·Satoshi Nishida·Takuya Niikawa·Katsunori Miyahara·Hitomi Yanaka
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Abstract

One of the main objectives in developing large vision-language models (LVLMs) is to engineer systems that can assist humans with multimodal tasks, including interpreting descriptions of perceptual experiences. A central phenomenon in this context is amodal completion, in which people perceive objects even when parts of those objects are hidden. Although numerous studies have assessed whether computer-vision algorithms can detect or reconstruct occluded regions, the inferential abilities of LVLMs on texts related to amodal completion remain unexplored. To address this gap, we constructed a benchmark grounded in Basic Formal Ontology to achieve a systematic classification of amodal completion. Our results indicate that while many LVLMs achieve human-comparable performance overall, their accuracy diverges for certain types of objects being completed. Notably, in certain categories, some LLaVA-NeXT variants and Claude 3.5 Sonnet exhibit lower accuracy on original images compared to blank stimuli lacking visual content. Intriguingly, this disparity emerges only under Japanese prompting, suggesting a deficiency in Japanese-specific linguistic competence among these models.

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Amane Watahiki, Tomoki Doi, Taiga Shinozaki, Satoshi Nishida, Takuya Niikawa, Katsunori Miyahara, Hitomi Yanaka. 2025-07-08. Bridging Perception and Language: A Systematic Benchmark for LVLMs' Understanding of Amodal Completion Reports. https://arxiv.org/abs/2507.05799

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