Invention Grant
- Patent Title: Learning semantic segmentation models in the absence of a portion of class labels
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Application No.: US17592378Application Date: 2022-02-03
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Publication No.: US12118788B2Publication Date: 2024-10-15
- Inventor: S Alireza Golestaneh , João D. Semedo , Filipe J. Cabrita Condessa , Wan-Yi Lin , Stefan Gehrer
- Applicant: Robert Bosch GmbH
- Applicant Address: DE Stuttgart
- Assignee: Robert Bosch GmbH
- Current Assignee: Robert Bosch GmbH
- Current Assignee Address: DE Stuttgart
- Agency: Brooks Kushman P.C.
- Main IPC: G06V20/40
- IPC: G06V20/40 ; G06V10/764

Abstract:
Performing semantic segmentation in an absence of labels for one or more semantic classes is provided. One or more weak predictors are utilized to obtain label proposals of novel classes for an original dataset for which at least a subset of sematic classes are unlabeled classes. The label proposals are merged with ground truth of the original dataset to generate a merged dataset, the ground truth defining labeled classes of portions of the original dataset. A machine learning model is trained using the merged dataset. The machine learning model is utilized for performing semantic segmentation on image data.
Public/Granted literature
- US20230245450A1 LEARNING SEMANTIC SEGMENTATION MODELS IN THE ABSENCE OF A PORTION OF CLASS LABELS Public/Granted day:2023-08-03
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