Invention Grant
- Patent Title: Weakly-supervised semantic segmentation with self-guidance
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Application No.: US16760096Application Date: 2018-10-09
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Publication No.: US11657274B2Publication Date: 2023-05-23
- Inventor: Kunpeng Li , Ziyan Wu , Kuan-Chuan Peng , Jan Ernst
- Applicant: Siemens Aktiengesellschaft
- Applicant Address: DE Munich
- Assignee: SIEMENS AKTIENGESELLSCHAFT
- Current Assignee: SIEMENS AKTIENGESELLSCHAFT
- Current Assignee Address: DE Munich
- International Application: PCT/US2018/054993 2018.10.09
- International Announcement: WO2019/089192A 2019.05.09
- Date entered country: 2020-04-29
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N5/04

Abstract:
Systems, methods, and computer-readable media are described for performing weakly supervised semantic segmentation of input images that utilizes self-guidance on attention maps during training to cause a guided attention inference network (GAIN) to focus attention on an object in an input image in a holistic manner rather than only on the most discriminative parts of the image. The self-guidance is provided jointly by a classification loss function and an attention mining loss function. Extra supervision can also be provided by using a select number pixel-level labeled input images to enhance the semantic segmentation capabilities of the GAIN.
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