论文标题
改善半监督对象检测的本地化
Improving Localization for Semi-Supervised Object Detection
论文作者
论文摘要
如今，半监督对象检测（SSOD）是一个热门话题，因为虽然收集用于创建新数据集的图像相当容易，但标记它们仍然是一项昂贵且耗时的任务。在半监督学习（SSL）设置上利用原始图像的成功方法之一是卑鄙的教师技术，在该技术中，老师的伪标记的运作以及从学生到教师的知识转移到教师的运作是同时进行的。但是，通过阈值进行伪标记并不是最好的解决方案，因为置信值与预测不确定性无关，不允许安全过滤预测。在本文中，我们引入了一项附加的分类任务，以进行边界框定位，以改善预测边界框的过滤并获得更高的学生培训质量。此外，我们从经验上证明，无监督部分上的边界框回归可以同样有助于培训与类别分类一样多。我们的实验表明，我们的IL-NET（改善本地化净）在限量注销方案中可可数据集上的SSOD性能提高了1.14％的AP。该代码可从https://github.com/implabunipr/unbiased-teacher/tree/ilnet获得
Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive and time-consuming task. One of the successful methods to take advantage of raw images on a Semi-Supervised Learning (SSL) setting is the Mean Teacher technique, where the operations of pseudo-labeling by the Teacher and the Knowledge Transfer from the Student to the Teacher take place simultaneously. However, the pseudo-labeling by thresholding is not the best solution since the confidence value is not strictly related to the prediction uncertainty, not permitting to safely filter predictions. In this paper, we introduce an additional classification task for bounding box localization to improve the filtering of the predicted bounding boxes and obtain higher quality on Student training. Furthermore, we empirically prove that bounding box regression on the unsupervised part can equally contribute to the training as much as category classification. Our experiments show that our IL-net (Improving Localization net) increases SSOD performance by 1.14% AP on COCO dataset in limited-annotation regime. The code is available at https://github.com/IMPLabUniPr/unbiased-teacher/tree/ilnet
