Exploring Spatial Diversity for Region-Based Active Learning
| Autor: | Lile Cai, Xun Xu, Chuan-Sheng Foo, Lining Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Pixel
Active learning (machine learning) business.industry Computer science Image segmentation Antenna diversity Machine learning computer.software_genre Semantics Computer Graphics and Computer-Aided Design Annotation Feature (machine learning) Segmentation Artificial intelligence business computer Software |
| Zdroj: | IEEE Transactions on Image Processing. 30:8702-8712 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2021.3120041 |
| Popis: | State-of-the-art methods for semantic segmentation are based on deep neural networks trained on large-scale labeled datasets. Acquiring such datasets would incur large annotation costs, especially for dense pixel-level prediction tasks like semantic segmentation. We consider region-based active learning as a strategy to reduce annotation costs while maintaining high performance. In this setting, batches of informative image regions instead of entire images are selected for labeling. Importantly, we propose that enforcing local spatial diversity is beneficial for active learning in this case, and to incorporate spatial diversity along with the traditional active selection criterion, e.g., data sample uncertainty, in a unified optimization framework for region-based active learning. We apply this framework to the Cityscapes and PASCAL VOC datasets and demonstrate that the inclusion of spatial diversity effectively improves the performance of uncertainty-based and feature diversity-based active learning methods. Our framework achieves 95% performance of fully supervised methods with only 5 - 9% of the labeled pixels, outperforming all state-of-the-art region-based active learning methods for semantic segmentation. |
| Databáze: | OpenAIRE |
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