Large Vision-Language Models (LVLMs) show significant strides in
general-purpose multimodal applications such as visual dialogue and embodied
navigation. However, existing multimodal evaluation benchmarks cover a limited
number of multimodal tasks testing rudimentary capabilities, falling short in
tracking LVLM development. In this study, we present MMT-Bench, a comprehensive
benchmark designed to assess LVLMs across massive multimodal tasks requiring
expert knowledge and deliberate visual recognition, localization, reasoning,
and planning. MMT-Bench comprises $31,325$ meticulously curated multi-choice
visual questions from various multimodal scenarios such as vehicle driving and
embodied navigation, covering $32$ core meta-tasks and $162$ subtasks in
multimodal understanding. Due to its extensive task coverage, MMT-Bench enables
the evaluation of LVLMs using a task map, facilitating the discovery of in- and
out-of-domain tasks. Evaluation results involving $30$ LVLMs such as the
proprietary GPT-4V, GeminiProVision, and open-sourced InternVL-Chat, underscore
the significant challenges posed by MMT-Bench. We anticipate that MMT-Bench
will inspire the community to develop next-generation multimodal foundation
models aimed at achieving general-purpose multimodal intelligence.

该研究介绍了 MMT-Bench，这是一个综合性评估基准，旨在评估大规模视觉 - 语言模型（LVLM）在多种跨领域的多模态任务上的能力，并促进下一代通用多模态智能基础模型的发展。