Interpolation-based semi-supervised learning for object detection.

Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), which considers and solves the problems caused by applying conventional Interpolation Regularization (IR) directly to object detection. We divide the output of the model into two types according to the objectness scores of both original patches that are mixed in IR. Then, we apply semi-supervised learning methods suitable for each type. This method dramatically improves the performance of semi-supervised learning as well as supervised learning. In the semi-supervised learning setting, our algorithm improves the current state-of-the-art performance on benchmark dataset (PASCAL VOC07 as labeled data and PASCAL VOC12 as unlabeled data) and benchmark architectures (SSD300 and SSD512). In the supervised learning setting, our method, trained with VOC07 as labeled data, improves the baseline methods by a significant margin, as well as shows better performance than the model that is trained using the previous state-of-the-art semi-supervised learning method using VOC07 as the labeled data and VOC12 + MSCOCO as the unlabeled data. Code is available at: this https URL .