In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of
the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily …

With the recent advancement in large language models (LLMs), there is a growing interest in
combining LLMs with multimodal learning. Previous surveys of multimodal large language …

Multi-modal large language models (MLLMs) have demonstrated remarkable success in
vision and visual-language tasks within the natural image domain. Owing to the significant …

We propose to use machine-generated instruction-following data to improve the zero-shot
capabilities of a large multimodal model with additional support for generative and image …

Augmenting large language models (LLMs) to understand audio--including non-speech
sounds and non-verbal speech--is critically important for diverse real-world applications of …

With the deepening of research on Large Language Models (LLMs), significant progress has
been made in recent years on the development of Large Multimodal Models (LMMs), which …

We introduce C3LLM (Conditioned-on-Three-Modalities Large Language Models), a novel
framework combining three tasks of video-to-audio, audio-to-text, and text-to-audio together …

This work uniquely identifies and characterizes four prevalent multimodal model
architectural patterns in the contemporary multimodal landscape. Systematically …

In recent years, with the realistic generation results and a wide range of personalized
applications, diffusion-based generative models gain huge attention in both visual and …