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

Elezi, Ismail and Yu, Zhiding and Anandkumar, Anima and Leal-Taixe, Laura and Alvarez, Jose M. (2022) Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ, pp. 14472-14781. ISBN 978-1-6654-6946-3.

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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 high-performing classes and lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified frame-work for active learning, that considers both the uncertainty and the robustness of the detector, ensuring that the network performs well in all classes. Furthermore, our method leverages auto-labeling to suppress a potential distribution drift while boosting the performance of the model. Experiments on PASCAL VOC07+12 and MS-COCO show that our method consistently outperforms a wide range of active learning methods, yielding up to a 7.7% improvement in mAP, or up to 82% reduction in labeling cost. Code is available at

Item Type:Book Section
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URLURL TypeDescription
Yu, Zhiding0000-0003-1776-996X
Anandkumar, Anima0000-0002-6974-6797
Leal-Taixe, Laura0000-0001-8709-1133
Record Number:CaltechAUTHORS:20230315-336419000.6
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:120064
Deposited By: George Porter
Deposited On:16 Mar 2023 19:13
Last Modified:16 Mar 2023 19:13

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