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Conference Paper
Selective Feature Learning with Filtering Out Noisy Objects in Background Images
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- Authors
- Soonyong Song, Heechul Bae, Hyonyoung Han, Youngsung Son
- Issue Date
- 2019-11
- Citation
- International Conference on Intelligent Robots and Systems (IROS) 2019, pp.1-2
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this competition, we propose a selective feature learning method to eliminate irrelevant objects in target images. We applied a Single Shot multibox Detection (SSD) algorithm to select desired objects. The SSD algorithm alleviates performance degradation by noisy objects. We trained SSD weights with annotated images in task 1. The refined dataset is fed into a traditional MobileNet classification network. We summarize our next research points through this competition in future works.
- KSP Keywords
- Feature Learning, Learning methods, Single-shot, performance degradation