Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection

• Published in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 14472 - 14481
• Main Authors: Elezi, Ismail, Yu, Zhiding, Anandkumar, Anima, Leal-Taixe, Laura, Alvarez, Jose M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2022
• Subjects: Costs; Detectors; Neural networks; Object detection; Robustness; Self-& semi-& meta- Emergency Reviews; Training; Uncertainty

Deep neural networks have reached high accuracy on object detection but their success hinges on large amounts of labeled data. To reduce the labels dependency, various active learning strategies have been proposed, based on the confidence of the detector. However, these methods are biased towards hi...