公开(公告)号：US20230081171A1

公开(公告)日：2023-03-16

申请号：US17467628

申请日：2021-09-07

Applicant: Google LLC

Inventor： Han Zhang ,  Jing Yu Koh ,  Jason Michael Baldridge ,  Yinfei Yang ,  Honglak Lee

IPC: G06T11/00 ,  G06K9/62 ,  G10L15/26 ,  G06N3/08

Abstract: A computer-implemented method includes receiving, by a computing device, a particular textual description of a scene. The method also includes applying a neural network for text-to-image generation to generate an output image rendition of the scene, the neural network having been trained to cause two image renditions associated with a same textual description to attract each other and two image renditions associated with different textual descriptions to repel each other based on mutual information between a plurality of corresponding pairs, wherein the plurality of corresponding pairs comprise an image-to-image pair and a text-to-image pair. The method further includes predicting the output image rendition of the scene.

公开(公告)号：US20250028173A1

公开(公告)日：2025-01-23

申请号：US18353590

申请日：2023-07-17

Applicant: Google LLC

Inventor： Saeid Rezaei ,  Daniel Corbalan ,  Andrew Logan ,  Jiwon Yang ,  Han Zhang

IPC: G02B27/01 ,  G02B1/11 ,  G02B1/14

Abstract: A cover is described for use in protecting near infrared sensors mounted to an augmented reality headset. Manufacturing the cover includes a polycarbonate injection molding process followed by a multi-coating deposition process. Contours of the sensor apparatus can be masked prior to the deposition to improve yield.

公开(公告)号：US20240265586A1

公开(公告)日：2024-08-08

申请号：US18564841

申请日：2022-05-27

Applicant: Google LLC

Inventor： Long Zhao ,  Han Zhang ,  Zizhao Zhang ,  Ting Chen

IPC: G06T11/00 ,  G06T3/4046

CPC classification number: G06T11/00 ,  G06T3/4046

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating high-resolution images using self-attention based neural networks. One of the systems includes a neural network configured to generate images, the neural network comprising a sequence of one or more first network blocks followed by a sequence of one or more second network blocks, wherein: each first network block is configured to perform operations comprising: applying a self-attention mechanism over at least a subset of first elements of a first block input to generate an updated first block input; and upsampling the updated first block input to generate a first block output; and each second network block is configured to perform operations comprising: processing a second block input using one or more neural network layers to generate an updated second block input; and upsampling the updated second block input to generate a second block output.

公开(公告)号：US20210089964A1

公开(公告)日：2021-03-25

申请号：US17026225

申请日：2020-09-19

Applicant: Google LLC

Inventor： Zizhao Zhang ,  Sercan Omer Arik ,  Tomas Jon Pfister ,  Han Zhang

IPC: G06N20/00 ,  G06N5/04

Abstract: A method for training a model comprises obtaining a set of labeled training samples each associated with a given label. For each labeled training sample, the method includes generating a pseudo label and estimating a weight of the labeled training sample indicative of an accuracy of the given label. The method also includes determining whether the weight of the labeled training sample satisfies a weight threshold. When the weight of the labeled training sample satisfies the weight threshold, the method includes adding the labeled training sample to a set of cleanly labeled training samples. Otherwise, the method includes adding the labeled training sample to a set of mislabeled training samples. The method includes training the model with the set of cleanly labeled training samples using corresponding given labels and the set of mislabeled training samples using corresponding pseudo labels.