Magic Leap, Inc.

Tarrence Van As

Andrew Rabinovich

Vijay Badrinarayanan

Prateek Singhal

Siddharth Mahendran

Khushi Gupta

Nitesh Sekhar

Manushree Gangwar

Adithya Shricharan Srinivasa Rao

Joelle Zimmermann

Nitin Bansal

Zhengyang Wu

Techniques related to the computation of gaze vectors of users of wearable devices are disclosed. A neural network may be trained through first and second training steps. The neural network may include a set of feature encoding layers and a plurality of sets of task-specific layers that each operate on an output of the set of feature encoding layers. During the first training step, a first image of a first eye may be provided to the neural network, eye segmentation data may be generated using the neural network, and the set of feature encoding layers may be trained. During the second training step, a second image of a second eye may be provided to the neural network, network output data may be generated using the neural network, and the plurality of sets of task-specific layers may be trained.

Eye tracking and gaze estimation using off-axis camera

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an object recognition neural network using multiple data sources. One of the methods includes receiving training data that includes a plurality of training images from a first source and images from a second source. A set of training images are obtained from the training data. For each training image in the set of training images, contrast equalization is applied to the training image to generate a modified image. The modified image is processed using the neural network to generate an object recognition output for the modified image. A loss is determined based on errors between, for each training image in the set, the object recognition output for the modified image generated from the training image and ground-truth annotation for the training image. Parameters of the neural network are updated based on the determined loss.

Object recognition neural network training using multiple data sources

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New York, NY, United States of America

Tarrence Van As