  Video understanding relies on perceiving the global content and modeling its
internal connections (e.g., causality, movement, and spatio-temporal
correspondence). To learn these interactions, we apply a mask-then-predict
pre-training task on discretized video tokens generated via VQ-VAE. Unlike
language, where the text tokens are more independent, neighboring video tokens
typically have strong correlations (e.g., consecutive video frames usually look
very similar), and hence uniformly masking individual tokens will make the task
too trivial to learn useful representations. To deal with this issue, we
propose a block-wise masking strategy where we mask neighboring video tokens in
both spatial and temporal domains. We also add an augmentation-free contrastive
learning method to further capture the global content by predicting whether the
video clips are sampled from the same video. We pre-train our model on
uncurated videos and show that our pre-trained model can reach state-of-the-art
results on several video understanding datasets (e.g., SSV2, Diving48). Lastly,
we provide detailed analyses on model scalability and pre-training method
design. Code is released at https://github.com/airsplay/vimpac.
