Meta Platforms, Inc.

Facebook, Inc.

Heng Wang

Lorenzo Torresani

Du Le Hong Tran

Kaiming He

Matthew Dan Feiszli

Gediminas Bertasius

Antoine Miech

In one embodiment, a method includes accessing a stream of F video frames, where each of the F video frames includes N patches that are non-overlapping, generating an initial embedding vector for each of the N×F patches in the F video frames, generating a classification embedding by processing the generated N×F initial embedding vectors using a self-attention-based machine-learning model that computes a temporal attention and a spatial attention for each of the N×F patches, and determining a class of the stream of video frames based on the generated classification embedding.

Classifying a video stream using a self-attention-based machine-learning model

In one embodiment, a method includes accessing a first set of images of multiple images of a scene, wherein the first set of images show the scene during a time period. The method includes generating, by processing the first set of images using a first machine-learning model, one or more attributes representing observed actions performed in the scene during the time period. The method includes predicting, by processing the generated one or more attributes using a second machine-learning model, one or more actions that would happen in the scene after the time period.

Anticipating future video based on present video

In one embodiment, a method includes receiving a request for information associated with a video, determining the information associated with the video by processing the video using a machine-learning model which is based on a convolutional neural network comprising a plurality of layers, wherein at least one of the plurality of layers comprises one or more building blocks, wherein at least one of the one or more building blocks comprises a first filter configured to perform a three-dimensional (3D) pointwise convolutional operation and a second filter configured to perform a three-dimensional (3D) groupwise convolutional operation, and outputting the information associated with the video in response to the request.

Convolutional neural network based on groupwise convolution for efficient video analysis

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Mountain View, CA, United States of America

Heng Wang