Apple Inc.

Xnor.ai, Inc.

Carlo Eduardo Cabanero Del Mundo

Hessam Bagherinezhad

Anish Jnyaneshwar Prabhu

Peter Zatloukal

Ali Farhadi

Lawrence Frederick Arnstein

Mohammad Rastegari

Maxwell Christian Horton

Sophie Lebrecht

Matthew S Weaver

Robert Stephen Karl, Jr

Alexander James Oscar Craver Kirchhoff

Daniel Carl Tormoen

In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates a moment of perception of the determined motion.

Detection of moment of perception

In one embodiment, a method includes providing, to a client system of a user, a user interface for display. The user interface may include a first set of options for selecting an artificial intelligence (AI) task for integrating into a user application, a second set of options for selecting one or more devices on which the user wants to deploy the selected AI task, and a third set of options for selecting one or more performance constraints specific to the selected devices. User specifications may be received based on user selections in the first, second, and third sets of options. A custom AI model may be generated based on the user specifications and sent to the client system of the user for integrating into the user application. The custom AI model once integrated may enable the user application to perform the selected AI task on the selected devices.

Model selection interface

In one embodiment, a method includes receiving an input video comprising a plurality of image frames including an object of interest. Based on the plurality of image frames, a motion associated with the object of interest is determined, and the plurality of image frames are classified using a machine-learning model to identify one of the plurality of image frames that indicates detection of the determined motion.

In one embodiment, a method includes receiving an input tensor corresponding to a media object at a binary convolutional neural network, wherein the binary convolutional neural network comprises at least one binary convolution layer comprising one or more weights, and wherein the media object is associated with a particular task, binarizing the input tensor by the at least one binary convolution layer, binarizing the one or more weights by the at least one binary convolution layer, and generating an output corresponding to the particular task by the binary convolutional neural network based on the binarized input tensor and the binarized one or more weights.

Optimizing binary convolutional neural networks

In one embodiment, a method includes receiving a machine-learning model trained to detect a specified motion using multiple videos, wherein each video has at least one frame labeled as a moment of perception of the specified motion, identifying an object-of-interest depicted in an input video, detecting a motion of the object-of-interest, determining that the detected motion is the specified motion, and classifying one of the frames of the input video as the moment of perception of the specified motion.

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Seattle, WA, United States of America

Carlo Eduardo Cabanero Del Mundo