Recently, semi-supervised learning attracts extensive interests in the field of object detection, since it is beneficial for alleviating the label annotation burden. Existing methods generates unsatisfactory pseudo labels and have imperfect …
Most of the recent research in semi-supervised object detection follows the pseudo-labeling paradigm evolved from the semi-supervised image classification task. However, the training paradigm of the two-stage object detector inevitably makes the …
In the semi-supervised object detection task, due to the scarcity of labeled data and the diversity and complexity of objects to be detected, the quality of pseudo-labels generated by existing methods for unlabeled data is relatively low, which …
Identifying and locating diseases in chest Xrays are very challenging, due to the low visual contrast between normal and abnormal regions, and distortions caused by other overlapping tissues. An interesting phenomenon is that there exist many similar …