Google Inc.

Waymo LLC

Dumitru Erhan

Alexander Toshev

Oriol Vinyals

Samy Bengio

Christian Szegedy

Dragomir Dimitrov Anguelov

Nathan Silberman

Dilip Krishnan

Konstantinos Bousmalis

George Trigeorgis

Qian Yu

Vinay Damodar Shet

Martin Christian Stumpe

Yin Zhou

Pei Sun

Yuning Chai

Sacha Christophe Arnoud

Henrik Kretzschmar

Liron Yatziv

Sean Rafferty

David Martin Dohan

Zhenpei Yang

Alexander Toshkov Toshev

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generated simulated sensor data. One of the methods includes obtaining a surfel map generated from sensor observations of a real-world environment and generating, for each surfel in the surfel map, a respective grid having a plurality of grid cells, wherein each grid has an orientation matching an orientation of a corresponding surfel, and wherein each grid cell within each grid is assigned a respective color value. For a simulated location within a simulated representation of the real-world environment, a textured surfel rendering is generated, including combining color information from grid cells visible from the simulated location within the simulated representation of the real-world environment.

Generating environmental data

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating descriptions of input images. One of the methods includes obtaining an input image; processing the input image using a first neural network to generate an alternative representation for the input image; and processing the alternative representation for the input image using a second neural network to generate a sequence of a plurality of words in a target natural language that describes the input image.

Generating natural language descriptions of images

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the system includes a shared encoder neural network implemented by one or more computers, wherein the shared encoder neural network is configured to: receive an input image from a target domain; and process the input image to generate a shared feature representation of features of the input image that are shared between images from the target domain and images from a source domain different from the target domain; and a classifier neural network implemented by the one or more computers, wherein the classifier neural network is configured to: receive the shared feature representation; and process the shared feature representation to generate a network output for the input image that characterizes the input image.

Domain separation neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the systems includes a domain transformation neural network implemented by one or more computers, wherein the domain transformation neural network is configured to: receive an input image from a source domain; and process a network input comprising the input image from the source domain to generate a transformed image that is a transformation of the input image from the source domain to a target domain that is different from the source domain.

Transforming source domain images into target domain images

Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Business discovery from imagery

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network to detect object in images. One of the methods includes receiving a training image and object location data for the training image; providing the training image to a neural network and obtaining bounding box data for the training image from the neural network, wherein the bounding box data comprises data defining a plurality of candidate bounding boxes in the training image and a respective confidence score for each candidate bounding box in the training image; determining an optimal set of assignments using the object location data for the training image and the bounding box data for the training image, wherein the optimal set of assignments assigns a respective candidate bounding box to each of the object locations; and training the neural network on the training image using the optimal set of assignments.

Training a neural network to detect objects in images

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting objects in images. One of the methods includes receiving an input image. A full object mask is generated by providing the input image to a first deep neural network object detector that produces a full object mask for an object of a particular object type depicted in the input image. A partial object mask is generated by providing the input image to a second deep neural network object detector that produces a partial object mask for a portion of the object of the particular object type depicted in the input image. A bounding box is determined for the object in the image using the full object mask and the partial object mask.

Object detection using deep neural networks

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San Francisco, CA, United States of America

Dumitru Erhan