Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to select the most informative images for detector training by observing instance-level uncertainty. MI-AOD defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict instance uncertainty of the unlabeled set. MI-AOD treats unlabeled images as instance bags and feature anchors in images as instances, and estimates the image uncertainty by re-weighting instances in a multiple instance learning (MIL) fashion. Iterative instance uncertainty learning and re-weighting facilitate suppressing noisy instances, toward bridging the gap between instance uncertainty and image-level uncertainty. Experiments validate that MI-AOD sets a solid baseline for instance-level active learning. On commonly used object detection datasets, MI-AOD outperforms state-of-the-art methods with significant margins, particularly when the labeled sets are small. Code is available at https://github.com/yuantn/MI-AOD.

翻译：尽管积极学习图像识别工作取得了实质性进展,但仍缺乏为对象检测指定的实例级积极学习方法。在本文件中,我们提议多例主动物体检测(MI-AOD),通过观察实例级不确定性选择最丰富的图像用于检测或培训。MI-AOD定义了一个实例不确定性学习模块,该模块利用了在标签上为预测未贴标签数据集的不确定性而培训的2个对抗性实例分类器的差异。MI-AOD将未贴标签的图像作为实例处理,作为图像中的实例包和特效锁定器,并估计图像的不确定性,以多例学习(MIL)方式进行重新加权。循环性实例不确定性学习和重新加权有助于抑制噪音事件,以弥合实例不确定性和图像级不确定性之间的差距。实验证实MI-AOD为实例级积极学习建立了坚实的基线。在常用的物体检测数据集中,MI-AOD将未贴标签的图像作为实例包和特效标记的状态方法,特别是在标签组体小的情况下。 https://gthrubub.com/yumant/cant/col可查到代码。