Go to ICLR 2025 Conference homepageMCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark
Keywords: Multimodal Benchmark, MLLM, OCR, Cognition, perception
Abstract: The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchmarks tailored to the scenario emphasize perceptual capabilities, while overlooking the assessment of cognitive abilities. To address this limitation, we introduce a $\textbf{M}$ultimodal benchmark towards $\textbf{T}$ext-rich visual scenes, to evaluate the $\textbf{C}$ognitive capabilities of MLLMs through visual reasoning and content-creation tasks ($\textbf{MCTBench}$). To mitigate potential evaluation bias from the varying distributions of datasets, MCTBench incorporates several perception tasks (e.g., scene text recognition) to ensure a consistent comparison of both the cognitive and perceptual capabilities of MLLMs. To improve the efficiency and fairness of content-creation evaluation, we conduct an automatic evaluation pipeline. Evaluations of various MLLMs on MCTBench reveal that, despite their impressive perceptual capabilities, their cognition abilities require enhancement. We hope MCTBench will offer the community an efficient resource to explore and enhance cognitive capabilities towards text-rich visual scenes.
Primary Area: datasets and benchmarks
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Submission Number: 10080
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