VideoPrism: A Foundational Visual Encoder for Video Understanding
Long Zhao Nitesh B. Gundavarapu Liangzhe Yuan Hao Zhou Shen Yan Jennifer J. Sun Luke Friedman Rui Qian Tobias Weyand Yue Zhao Rachel Hornung Florian Schroff Ming-Hsuan Yang David A. Ross Huisheng Wang Hartwig Adam Mikhail Sirotenko Ting Liu Boqing Gong
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 30 out of 33 video understanding
benchmarks.