We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips with noisy parallel text (e.g., ASR transcripts). The pretraining approach improves upon masked autoencoding by global-local distillation of semantic video embeddings and a token shuffling scheme, enabling VideoPrism to focus primarily on the video modality while leveraging the invaluable text associated with videos. We extensively test VideoPrism on four broad groups of video understanding tasks, from web video question answering to CV for science, achieving state-of-the-art performance on 31 out of 33 video understanding benchmarks. Our models are released at https://github.com/google-deepmind/videoprism.
@article{arxiv.2402.13217,
title = {VideoPrism: A Foundational Visual Encoder for Video Understanding},
author = {Long Zhao and Nitesh B. Gundavarapu and Liangzhe Yuan and Hao Zhou and Shen Yan and Jennifer J. Sun and Luke Friedman and Rui Qian and Tobias Weyand and Yue Zhao and Rachel Hornung and Florian Schroff and Ming-Hsuan Yang and David A. Ross and Huisheng Wang and Hartwig Adam and Mikhail Sirotenko and Ting Liu and Boqing Gong},
journal= {arXiv preprint arXiv:2402.13217},
year = {2025}
}
Comments
Accepted to ICML 2024. v2: added retrieval results on MSRVTT (1K-A), more data analyses, and ablation studies; v3: released models at https://github.com/google-deepmind/videoprism