  Contrastive language-image pretraining has shown great success in learning
visual-textual joint representation from web-scale data, demonstrating
remarkable "zero-shot" generalization ability for various image tasks. However,
how to effectively expand such new language-image pretraining methods to video
domains is still an open problem. In this work, we present a simple yet
effective approach that adapts the pretrained language-image models to video
recognition directly, instead of pretraining a new model from scratch. More
concretely, to capture the long-range dependencies of frames along the temporal
dimension, we propose a cross-frame attention mechanism that explicitly
exchanges information across frames. Such module is lightweight and can be
plugged into pretrained language-image models seamlessly. Moreover, we propose
a video-specific prompting scheme, which leverages video content information
for generating discriminative textual prompts. Extensive experiments
demonstrate that our approach is effective and can be generalized to different
video recognition scenarios. In particular, under fully-supervised settings,
our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using
12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot
experiments, our approach surpasses the current state-of-the-art methods by
+7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In
few-shot scenarios, our approach outperforms previous best methods by +32.1%
and +23.1% when the labeled data is extremely limited. Code and models are
available at https://aka.ms/X-CLIP
