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 …

Abstract We present Composable Diffusion (CoDi), a novel generative model capable of
generating any combination of output modalities, such as language, image, video, or audio …

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 …

This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables
learning powerful and transferable video-text representations for multimodal understanding …

Multimodal large language models (MLLMs) have gained significant attention due to their
strong multimodal understanding capability. However existing works rely heavily on modality …

The video-language (VL) pretraining has achieved remarkable improvement in multiple
downstream tasks. However, the current VL pretraining framework is hard to extend to …

With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

The performance of text-video retrieval has been significantly improved by vision-language
cross-modal learning schemes. The typical solution is to directly align the global video-level …

Abstract We present CoDi-2 a Multimodal Large Language Model (MLLM) for learning in-
context interleaved multimodal representations. By aligning modalities with language for …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …