We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses
on core visual perception abilities not found in other evaluations. Most of the Blink tasks can …

Despite significant progress in generative AI, comprehensive evaluation remains
challenging because of the lack of effective metrics and standardized benchmarks. For …

The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with
large language models (LLMs) and computer vision (CV) systems driving advancements in …

The revolutionary capabilities of large language models (LLMs) have paved the way for
multimodal large language models (MLLMs) and fostered diverse applications across …

Rapid advancements have been made in extending Large Language Models (LLMs) to
Large Multi-modal Models (LMMs). However, extending input modality of LLMs to video data …

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language
Models (MLLMs) have garnered increased attention from both industry and academia …

Large Vision-Language Models (LVLMs) have received widespread attention for advancing
the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted …

This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline
Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously …

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning
system by integrating powerful Large Language Models (LLMs) with various modality …

Current benchmarks for assessing vision-language models (VLMs) often focus on their
perception or problem-solving capabilities and neglect other critical aspects such as …