Active Learning for Deep Object Detection
This work addresses the problem of efficient data labeling for object detection tasks, but it is incremental as it builds on existing active learning and object detection methods.
The paper tackles the challenge of labeling costs in deep object detection by proposing uncertainty-based active learning metrics and a class imbalance-aware selection method, achieving systematic evaluation on PASCAL VOC 2012.
The great success that deep models have achieved in the past is mainly owed to large amounts of labeled training data. However, the acquisition of labeled data for new tasks aside from existing benchmarks is both challenging and costly. Active learning can make the process of labeling new data more efficient by selecting unlabeled samples which, when labeled, are expected to improve the model the most. In this paper, we combine a novel method of active learning for object detection with an incremental learning scheme to enable continuous exploration of new unlabeled datasets. We propose a set of uncertainty-based active learning metrics suitable for most object detectors. Furthermore, we present an approach to leverage class imbalances during sample selection. All methods are evaluated systematically in a continuous exploration context on the PASCAL VOC 2012 dataset.