MEAL: Manifold Embedding-based Active Learning
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Image segmentation is a common and challenging task in autonomous driving. Availability of sufficient pixel-level annotations for the training data is a hurdle. Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling. In this work, we propose a new pool-based method for active learning, which proposes promising image regions, in each acquisition step. The problem is framed in an exploration-exploitation framework by combining an embedding based on Uniform Manifold Approximation to model representativeness with entropy as uncertainty measure to model informativeness. We applied our proposed method to the challenging autonomous driving data sets CamVid and Cityscapes and performed a quantitative comparison with state-of-the-art methods. We find that our active learning method achieves better performance on CamVid compared to other methods, while on Cityscapes, the performance lift was negligible.
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