Google Inc.

Aaron Joseph Maschinot

Ce Liu

Dilip Krishnan

Michael Krainin

Aaron Yehuda Sarna

Piotr Teterwak

Daniel Pettigrew

Michael Rubinstein

Jingyu Cui

Chen Wang

Julia Winn

Abraham Stephens

Yonglong Tian

Prannay Khosla

Jingbin Wang

Mikael Bonnevie

Hossein Talebi

Phillip John Isola

Philip John Isola

Shuchao Bi

Wenchao Tong

Haifeng Gong

Raanan Sayag

Michael Spencer Krainin

Mikael Pierre Bonnevie

The present disclosure provides an improved training methodology that enables supervised contrastive learning to be simultaneously performed across multiple positive and negative training examples. In particular, example aspects of the present disclosure are directed to an improved, supervised version of the batch contrastive loss, which has been shown to be very effective at learning powerful representations in the self-supervised setting. Thus, the proposed techniques adapt contrastive learning to the fully supervised setting and also enable learning to occur simultaneously across multiple positive examples.

Supervised contrastive learning with multiple positive examples

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating realistic extensions of images. In one aspect, a method comprises providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters. The generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image. The extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be a realistic extension of the provided image. The generative neural network is trained using an adversarial loss objective function.

Image extension neural networks

Some implementations relate to determining whether glare is present in captured image(s) of an object (e.g., a photo) and/or to determining one or more attributes of any present glare. Some of those implementations further relate to adapting one or more parameters for a glare removal process based on whether the glare is determined to be present and/or based on one or more of the determined attributes of any glare determined to be present. Some additional and/or alternative implementations disclosed herein relate to correcting color of a flash image of an object (e.g., a photo). The flash image is based on one or more images captured by a camera of a client device with a flash component of the client device activated. In various implementations, correcting the color of the flash image is based on a determined color space of an ambient image of the object.

Adaptive glare removal and/or color correction

Growing community of inventors

Somerville, MA, United States of America

Aaron Joseph Maschinot