# Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection Source: [Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection]() Collection index: [llms.txt]() Data updated: 2026-01-04T05:07:27.949Z Authors: Ismail Elezi, Zhiding Yu, Anima Anandkumar, Laura Leal-Taixe, Jose M. Alvarez Published: 2021-06-22T16:53:09.000Z ## Abstract 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, typically based on the confidence of the detector. However, these methods are biased towards high-performing classes and can lead to acquired datasets that are not good representatives of the testing set data. In this work, we propose a unified framework 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 will be released upon acceptance of the paper. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()