论文标题
用稀疏注释的数据集进行半监督对象检测
Semi-Supervised Object Detection with Sparsely Annotated Dataset
论文作者
论文摘要
在基于卷积神经网络的训练对象探测器中，选择有效的积极示例进行训练是一个重要因素。但是，当训练基于锚的探测器对图像上的稀疏注释时，寻找有效的积极例子会阻碍训练性能。当使用基于锚的培训对地面真相边界框中收集给定的正面示例时，通常可以在当前培训类中包括来自其他类别的对象，或者需要接受培训的对象只能作为负面示例来取样。我们使用了两种方法来解决此问题：1）使用无锚对象检测器和2）使用单个对象跟踪器的半监督基于学习的对象检测。提出的技术通过使用稀疏注释的边界框作为连续帧的时间域中的锚点执行单个对象跟踪。从跟踪结果中，以自动化的方式生成训练图像的密集注释，并用于训练对象检测器。我们将基于单一对象跟踪的半监督学习应用于Epic-Kitchens数据集。结果，我们能够在看不见的部分中实现\ textbf {亚军}的性能，同时在Epic-kitchens 2020 2020 iOU> 0.5评估的Epic-kitchens 2020对象检测挑战中获得第一名
In training object detector based on convolutional neural networks, selection of effective positive examples for training is an important factor. However, when training an anchor-based detectors with sparse annotations on an image, effort to find effective positive examples can hinder training performance. When using the anchor-based training for the ground truth bounding box to collect positive examples under given IoU, it is often possible to include objects from other classes in the current training class, or objects that are needed to be trained can only be sampled as negative examples. We used two approaches to solve this problem: 1) the use of an anchorless object detector and 2) a semi-supervised learning-based object detection using a single object tracker. The proposed technique performs single object tracking by using the sparsely annotated bounding box as an anchor in the temporal domain for successive frames. From the tracking results, dense annotations for training images were generated in an automated manner and used for training the object detector. We applied the proposed single object tracking-based semi-supervised learning to the Epic-Kitchens dataset. As a result, we were able to achieve \textbf{runner-up} performance in the Unseen section while achieving the first place in the Seen section of the Epic-Kitchens 2020 object detection challenge under IoU > 0.5 evaluation
