Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …

In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

We present ImageBind, an approach to learn a joint embedding across six different
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …

Addressing the dual challenges of local redundancy and global dependencies in video
understanding, this work innovatively adapts the Mamba to the video domain. The proposed …

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 Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-
shot classification, text-image retrieval, and text-image generation by aligning image and …

The foundation models have recently shown excellent performance on a variety of
downstream tasks in computer vision. However, most existing vision foundation models …

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 …

Recent years have witnessed a big convergence of language, vision, and multi-modal
pretraining. In this work, we present mPLUG-2, a new unified paradigm with modularized …

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