Active learning has been demonstrated effective to reduce labeling cost, while most progress has been designed for image recognition, there still lacks instance-level active learning for object detection. In this paper, we rethink two key components, i.e., localization and recognition, for object detection, and find that the correctness of them are highly related, therefore, it is not necessary to annotate both boxes and classes if we are given pseudo annotations provided with the trained model. Motivated by this, we propose an efficient query strategy, termed as DeLR, that Decoupling the Localization and Recognition for active query. In this way, we are probably free of class annotations when the localization is correct, and able to assign the labeling budget for more informative samples. There are two main differences in DeLR: 1) Unlike previous methods mostly focus on image-level annotations, where the queried samples are selected and exhausted annotated. In DeLR, the query is based on region-level, and we only annotate the object region that is queried; 2) Instead of directly providing both localization and recognition annotations, we separately query the two components, and thus reduce the recognition budget with the pseudo class labels provided by the model. Experiments on several benchmarks demonstrate its superiority. We hope our proposed query strategy would shed light on researches in active learning in object detection.

翻译：主动学习已被证明能有效降低标注成本，但现有研究多聚焦于图像识别领域，目标检测中的实例级主动学习仍存在空白。本文重新审视了目标检测的两个关键组成部分，即定位与识别，发现二者的正确性高度相关。因此，若利用训练模型提供伪标注，则无需同时标注边界框和类别。基于此启发，我们提出一种高效查询策略DeLR，通过解耦定位与识别来实现主动查询。这种方法在定位正确时可免除类别标注，从而将标注预算分配给更多信息量更大的样本。DeLR具有两大核心差异：1）不同于以往主要关注图像级标注的方法（选中样本需全部标注），DeLR基于区域级查询，仅标注被查询的目标区域；2）DeLR不直接同时提供定位与识别标注，而是分别查询两个组件，利用模型提供的伪类别标签减少识别标注预算。多个基准实验验证了其优越性。希望所提查询策略能为目标检测中的主动学习研究提供新思路。