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arXiv · 2412.09603

Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior

Jiaying Lin·Shuquan Ye·Dan Xu·Wanli Ouyang·Rynson W. H. Lau
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Abstract

While Multimodal Large Language Models (MLLMs) excel at many vision tasks, it is unknown if they exhibit human-like perceptual behaviors. To evaluate this, we introduce HVSBench, the first large-scale benchmark with over 85,000 samples designed to test MLLM alignment with the human visual system (HVS). The benchmark covers 13 categories across 5 key fields: Prominence, Subitizing, Prioritizing, Free-Viewing, and Searching. Our comprehensive evaluation reveals a significant perceptual gap: even state-of-the-art MLLMs achieve only moderate results. In contrast, human participants demonstrate strong performance, significantly outperforming all models. This underscores the high quality of HVSBench and the need for more human-aligned AI. We believe our benchmark will be a critical tool for developing the next generation of explainable MLLMs.

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Jiaying Lin, Shuquan Ye, Dan Xu, Wanli Ouyang, Rynson W. H. Lau. 2024-12-12. Do MLLMs Exhibit Human-like Perceptual Behaviors? HVSBench: A Benchmark for MLLM Alignment with Human Perceptual Behavior. https://arxiv.org/abs/2412.09603

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