Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

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 …

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 …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Foundation models (FMs) are large-scale deeplearning models that are developed using
large datasets and self-supervised learning methods. These models serve as a base for …

Majority of research in learning based methods has been towards designing and training
networks for specific tasks. However, many of the learning based tasks, across modalities …

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