Google Inc.

Nevengineering, Inc.

Hartwig Adam

Hartmut Neven

David Petrou

Matthew John Bridges

Yuan Hang Li

John Patrick Flynn

Teresa Ko

Alexei Masterov

Shailesh Nalawadi

Matthew Ryan Casey

Ulrich Franz Buddemeier

Andrew Logan Harp

Fernando A Brucher

Adam L Berenzweig

Andrews-Junior Kimbembe

Mikkel Crone Koser

Paul Chang

Li Zhang

Charles Joseph Rosenberg

Andrew Rabinovich

Yang Song

Ming Zhao

Anand Pillai

Henry Allan Rowley

Yushi Jing

Ullas Gargi

Taehee Lee

James William Philbin

Luc Vincent

Gabriel Taubman

Dan Fredinburg

Andrea Lynn Frome

Bo Wu

Corinna Cortes

Ahmad A Abdulkader

Ameesh Makadia

Troy Chinen

Jonathan McPhie

Leon G Palm

Laura Garcia-Barrio

Johannes Steffens

Marco Zennaro

Nemanja Petrovic

Alessandro Fin

Boris Babenko

Jeffrey M Gilbert

Maks Ovsjanikov

Sebastian Pueblas

Dragomir Dimitrov Anguelov

Alessandro Bissacco

Yuzo Watanabe

Henrik C Stewenius

Gerhard Florian Schroff

David Karam

Liang-Chieh Chen

Mark Cummins

Rafael Spring

Ariel Gilder

German Cheung

Eric Penner

Farooq Ahmad

Georgios Papandreou

Andrew Harp

John J Flynn

Michael Horowitz

Jonathon Shlens

Matthew E Kulick

David A Ross

Jignashu Parikh

Barret Zoph

Keith Shoji Kiyohara

Peng Wang

Arcot J Preetham

Henry Benjamin

Jia H Deng

Nan Ding

Sylvain Gelly

Zhongli Ding

Kong Man Cheung

Randall G Keller

Yukun Zhu

Brian Potetz

Farhana Bandukwala

Marco Andreetto

Yantao Zheng

Krzysztof Sikora

Brian Westphal

Tobias Magnusson

Gavin Doughtie

Hong-Kien Kenneth Ong

Wenze Hu

Jacob Smullyan

Harmut Neven

Alessandro Bissallo

Maxwell Donald Emmet Collins

Johannes B Steffens

Alexander Hermans

A system and method of identifying objects is provided. In one aspect, the system and method includes a hand-held device with a display, camera and processor. As the camera captures images and displays them on the display, the processor compares the information retrieved in connection with one image with information retrieved in connection with subsequent images. The processor uses the result of such comparison to determine the object that is likely to be of greatest interest to the user. The display simultaneously displays the images the images as they are captured, the location of the object in an image, and information retrieved for the object.

System and method of identifying visual objects

The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.

Methods and systems for processing imagery

Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

Efficient image analysis

Implementations of the present disclosure include actions of receiving image data of an image capturing a scene, receiving data describing one or more entities determined from the scene, the one or more entities being determined from the scene, determining one or more actions based on the one or more entities, each action being provided at least partly based on search results from searching the one or more entities, and providing instructions to display an action interface comprising one or more action elements, each action element being to induce execution of a respective action, the action interface being displayed in a viewfinder.

Smart camera user interface

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for instance segmentation. In one aspect, a system generates: (i) data identifying one or more regions of the image, wherein an object is depicted in each region, (ii) for each region, a predicted type of object that is depicted in the region, and (iii) feature channels comprising a plurality of semantic channels and one or more direction channels. The system generates a region descriptor for each of the one or more regions, and provides the region descriptor for each of the one or more regions to a segmentation neural network that processes a region descriptor for a region to generate a predicted segmentation of the predicted type of object depicted in the region.

Instance segmentation

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating still images and videos. One method includes receiving a plurality of images and a plurality of videos and determining whether the images are related to the videos. The determining includes, for an image and a video, extracting features from the image and extracting features frames of the video, and comparing the features to determine whether the image is related to the video. The method further includes maintaining a data store storing data associating each image with each video determined to be related to the image.

Associating still images and videos

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.

Neural architecture search for dense image prediction tasks

Growing community of inventors

Marina Del Rey, CA, United States of America

Hartwig Adam