Large language models (LLMs) have dramatically enhanced the field of language
intelligence, as demonstrably evidenced by their formidable empirical performance across a …

Solving complicated AI tasks with different domains and modalities is a key step toward
artificial general intelligence. While there are numerous AI models available for various …

We present Video-LLaMA, a multi-modal framework that empowers Large Language Models
(LLMs) with the capability of understanding both visual and auditory content in the video …

Abstract Multi-modal Large Language Models (MLLMs) have demonstrated impressive
instruction abilities across various open-ended tasks. However previous methods have …

We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new
capabilities of perceiving object descriptions (eg, bounding boxes) and grounding text to the …

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides,
they mostly fall prey to the limitation of only input-side multimodal understanding, without the …

In-context vision and language models like Flamingo support arbitrarily interleaved
sequences of images and text as input. This format not only enables few-shot learning via …

With the rapid development of Multi-modal Large Language Models (MLLMs) a number of
diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities …

Despite recent advances in text-to-3D generative methods there is a notable absence of
reliable evaluation metrics. Existing metrics usually focus on a single criterion each such as …

Scaling sequence length has become a critical demand in the era of large language models.
However, existing methods struggle with either computational complexity or model …