Does content-adaptive tokenization improve robustness and accuracy on the MMBench and MME benchmarks under var

Abstract

Abstract: Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance for diagnosis and treatment. Before that, it is crucial to develop benchmarks to evaluate LVLMs' effectiveness in various medical applications. Current benchmarks are often built upon specific academic literature, mainly focusing on a single domain, and lacking varying perceptual granularities. Thus, they face specific challenges, including limited

Research Question

Does content-adaptive tokenization improve robustness and accuracy on the MMBench and MME benchmarks under varying image resolutions compared to fixed-patch baselines when scaling the vision encoder from ViT-L to ViT-g?

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