公开(公告)号：US20240095927A1

公开(公告)日：2024-03-21

申请号：US18255186

申请日：2021-03-04

Applicant: Google LLC

Inventor： Jonathan Chung-Kuan Huang ,  Vighnesh Nandan Birodkar ,  Siyang Li ,  Zhichao Lu ,  Vivek Rathod

IPC: G06T7/11 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06T7/11 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G06T2207/20021 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132

Abstract: A computer-implemented method for partially supervised image segmentation having improved strong mask generalization includes obtaining, by a computing system including one or more computing devices, a machine-learned segmentation model, the machine-learned segmentation model including an anchor-free detector model and a deep mask head network, the deep mask head network including an encoder-decoder structure having a plurality of layers. The computer-implemented method includes obtaining, by the computing system, input data including tensor data. The computer-implemented method includes providing, by the computing system, the input data as input to the machine-learned segmentation model. The computer-implemented method includes receiving, by the computing system, output data from the machine-learned segmentation model, the output data including a segmentation of the tensor data, the segmentation including one or more instance masks.

公开(公告)号：US11853892B2

公开(公告)日：2023-12-26

申请号：US17252663

申请日：2019-07-10

Applicant: Google LLC

Inventor： Matthew Alun Brown ,  Jonathan Chung-Kuan Huang ,  Tal Remez

IPC: G06T7/194 ,  G06T7/11 ,  G06T11/20 ,  G06V10/764 ,  G06V10/82 ,  G06N3/084 ,  G06N3/045

CPC classification number: G06N3/084 ,  G06N3/045 ,  G06T7/11 ,  G06T7/194 ,  G06T11/20 ,  G06V10/764 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2210/12

Abstract: Example aspects of the present disclosure are directed to systems and methods that enable weakly-supervised learning of instance segmentation by applying a cut-and-paste technique to training of a generator model included in a generative adversarial network. In particular, the present disclosure provides a weakly-supervised approach to object instance segmentation. In some implementations, starting with known or predicted object bounding boxes, a generator model can learn to generate object masks by playing a game of cut-and-paste in an adversarial learning setup.

公开(公告)号：US20210256707A1

公开(公告)日：2021-08-19

申请号：US17252663

申请日：2019-07-10

Applicant: Google LLC

Inventor： Matthew Alun Brown ,  Jonathan Chung-Kuan Huang ,  Tal Remez

IPC: G06T7/194 ,  G06N3/04 ,  G06T7/11 ,  G06T11/20

Abstract: Example aspects of the present disclosure are directed to systems and methods that enable weakly-supervised learning of instance segmentation by applying a cut-and-paste technique to training of a generator model included in a generative adversarial network. In particular, the present disclosure provides a weakly-supervised approach to object instance segmentation. In some implementations, starting with known or predicted object bounding boxes, a generator model can learn to generate object masks by playing a game of cut-and-paste in an adversarial learning setup.