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language:
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pipeline_tag: VQA Benchmark
tags:
- visual perception
- benchmark
- VQA
- LVLM
- MLLM
MVP-Bench
MVP-Bench is a benchmark for evaluating the multi-level visual perception competence of Large Visual-Language Models (LVLMs). It was introduced in the paper MVP-Bench: Can Large Vision-Language Models Conduct Multi-level Visual Perception Like Humans and first released in this repository.
Benchmark description
MVP-Bench consists of 520 image pairs and 1872 questions. For evaluating LVLMs' visual perception at both levels, the 1872 questions contain 1105 high-level and 767 low-level questions. Besides, another novelty of this benchmark is assessing LVLMs' capability to conduct visual perception according to the correct visual clues and discern various visual perceptions between an image pair. Hence, we design 454 cross-image questions and 1418 single-image questions to form the question set. In terms of the question types, 872 of them are multiple-choice questions and 1000 of them are Yes/No questions.
Intended uses & limitations
The benchmark is only used for scientific research. To avoid any misuse of MVP-Bench, we will impose strict rules for access requirements and frequently track the follow-up works to constrain its usage within research-only goals.