Semi-Supervised Object Detection With Unlabeled Data

Besides the fully supervised object detection, many approaches have tried other training settings such as weakly-supervised learning which uses only weak labels (image-level) or mix-supervised learning which uses few strong labels (instance-level) and many weak labels. In our work, we investigate the semi-supervised learning with few instance-level labeled images and many unlabeled images. Considering the training of unlabeled images as a latent variable model, we propose an Expectation-Maximization method for semi-supervised object detection with unlabeled images. We estimate the latent labels and optimize the model for both classification part and localization part of object detection. Implementing our method on the one-stage object detection model YOLO, we show that like the weakly labeled images, the unlabeled images also can boost the performance of the detector by empirical experimentation on the Pascal VOC dataset.