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 rapid evolution of multimodal foundation model has demonstrated significant
progresses in vision-language understanding and generation, eg, our previous work SEED …

Benchmarks for large multimodal language models (MLMs) now serve to simultaneously
assess the general capabilities of models instead of evaluating for a specific capability. As a …

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 …

Abstract Autoregressive Visual Language Models (VLMs) demonstrate remarkable few-shot
learning capabilities within a multimodal context. Recently, multimodal instruction tuning has …

Large multimodal models (LMMs) have proven flexible and generalisable across many tasks
and fields. Although they have strong potential to aid scientific research, their capabilities in …

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