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 …

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 …

Abstract Video Foundation Models (VFMs) have received limited exploration due to high
computational costs and data scarcity. Previous VFMs rely on Image Foundation Models …

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

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and
language model, both in terms of size of the components and the breadth of its training task …

The quality of the data and annotation upper-bounds the quality of a downstream model.
While there exist large text corpora and image-text pairs high-quality video-text data is much …

Abstract Empowered by Large Language Models (LLMs), recent advancements in Video-
based LLMs (VideoLLMs) have driven progress in various video understanding tasks. These …

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 …

Understanding long real-world videos requires modeling of long-range visual
dependencies. To this end we explore video-first architectures building on the common …

The canonical approach to video-text retrieval leverages a coarse-grained or fine-grained
alignment between visual and textual information. However, retrieving the correct video …