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Augmented Feedback in Semantic Segmentation Under Image Level Supervision
- Source :
- Computer Vision – ECCV 2016 ISBN: 9783319464831, ECCV (8)
- Publication Year :
- 2016
- Publisher :
- Springer International Publishing, 2016.
-
Abstract
- Training neural networks for semantic segmentation is data hungry. Meanwhile annotating a large number of pixel-level segmentation masks needs enormous human effort. In this paper, we propose a framework with only image-level supervision. It unifies semantic segmentation and object localization with important proposal aggregation and selection modules. They greatly reduce the notorious error accumulation problem that commonly arises in weakly supervised learning. Our proposed training algorithm progressively improves segmentation performance with augmented feedback in iterations. Our method achieves decent results on the PASCAL VOC 2012 segmentation data, outperforming previous image-level supervised methods by a large margin.
- Subjects :
- Artificial neural network
Computer science
business.industry
Supervised learning
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020207 software engineering
02 engineering and technology
Pascal (programming language)
Machine learning
computer.software_genre
Augmented feedback
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Segmentation
Artificial intelligence
business
computer
computer.programming_language
Subjects
Details
- ISBN :
- 978-3-319-46483-1
- ISBNs :
- 9783319464831
- Database :
- OpenAIRE
- Journal :
- Computer Vision – ECCV 2016 ISBN: 9783319464831, ECCV (8)
- Accession number :
- edsair.doi...........00f39ea5d6c20d49af0e5b832b34fb9f
- Full Text :
- https://doi.org/10.1007/978-3-319-46484-8_6