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 …

In an era defined by the explosive growth of data and rapid technological advancements,
Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence …

We introduce InternVideo2, a new family of video foundation models (ViFM) that achieve the
state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue. Our …

In the domain of audio processing, Transfer Learning has facilitated the rise of Self-
Supervised Learning and Zero-Shot Learning techniques. These approaches have led to …

Vision and text have been fully explored in contemporary video-text foundational models,
while other modalities such as audio and subtitles in videos have not received sufficient …

The advancement of audio-language (AL) multimodal learning tasks has been significant in
recent years, yet the limited size of existing audio-language datasets poses challenges for …

Recent advancements in Multimodal Large Language Models (MLLMs) underscore the
significance of scalable models and data to boost performance, yet this often incurs …

Building general-purpose models that can perceive diverse real-world modalities and solve
various tasks is an appealing target in artificial intelligence. In this paper, we present …

Large language models (LLMs) have significantly advanced audio processing through
audio codecs that convert audio into discrete tokens, enabling the application of language …

We consider the task of generating diverse and realistic videos guided by natural audio
samples from a wide variety of semantic classes. For this task, the videos are required to be …