Google Inc.

Susanna Maria Ricco

Sudheendra Vijayanarasimhan

Bryan Andrew Seybold

George Dan Toderici

Nitin Khandelwal

Sanketh Shetty

Rahul Sukthankar

Balakrishnan Varadarajan

Weilong Yang

Tomas Izo

Apostol Ivanov Natsev

Min-Hsuan Tsai

Nicola Muscettola

Cordelia Luise Schmid

Phuong B Le

Sami Ahmad Abu-El-Haija

WeiHsin Gu

Aikaterini Fragkiadaki

David A Ross

Caroline Rebecca Pantofaru

Kevin Patrick Murphy

A system comprising an encoder neural network, a scene structure decoder neural network, and a motion decoder neural network. The encoder neural network is configured to: receive a first image and a second image; and process the first image and the second image to generate an encoded representation of the first image and the second image. The scene structure decoder neural network is configured to process the encoded representation to generate a structure output characterizing a structure of a scene depicted in the first image. The motion decoder neural network configured to process the encoded representation to generate a motion output characterizing motion between the first image and the second image.

Determining structure and motion in images using neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing a machine-learned object tracking policy. One of the methods includes receiving a current video frame by a user device having a plurality of installed object trackers, wherein each object tracker is configured to perform a different object tracking procedure on the current video frame rent video frame. The current video frame and one or more object tracks previously generated by the one or more object trackers are provided as input to a trained policy engine that implements a reinforcement learning model to generate a particular object tracking plan. A particular object tracking plan is selected based on the output of the reinforcement learning model, and the selected object tracking plan is performed on the current video frame to generate one or more updated object tracks for the current video frame.

Adaptive object tracking policy

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.

Processing images to localize novel objects

A computer-implemented method for selecting representative frames for videos is provided. The method includes receiving a video and identifying a set of features for each of the frames of the video. The features including frame-based features and semantic features. The semantic features identifying likelihoods of semantic concepts being present as content in the frames of the video. A set of video segments for the video is subsequently generated. Each video segment includes a chronological subset of frames from the video and each frame is associated with at least one of the semantic features. The method generates a score for each frame of the subset of frames for each video segment based at least on the semantic features, and selecting a representative frame for each video segment based on the scores of the frames in the video segment. The representative frame represents and summarizes the video segment.

Selecting and presenting representative frames for video previews

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Redwood City, CA, United States of America

Susanna Maria Ricco