Invention Grant
- Patent Title: Learning to segment via cut-and-paste
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Application No.: US17252663Application Date: 2019-07-10
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Publication No.: US11853892B2Publication Date: 2023-12-26
- Inventor: Matthew Alun Brown , Jonathan Chung-Kuan Huang , Tal Remez
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: GOOGLE LLC
- Current Assignee: GOOGLE LLC
- Current Assignee Address: US CA Mountain View
- Agency: Dority & Manning, P.A.
- International Application: PCT/US2019/041103 2019.07.10
- International Announcement: WO2020/014294A 2020.01.16
- Date entered country: 2020-12-15
- Main IPC: G06T7/194
- IPC: G06T7/194 ; G06T7/11 ; G06T11/20 ; G06V10/764 ; G06V10/82 ; G06N3/084 ; G06N3/045

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.
Public/Granted literature
- US20210256707A1 Learning to Segment via Cut-and-Paste Public/Granted day:2021-08-19
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