Here's an example of what I had in mind. Pseudolabels for unlabeled data are generated on the clean images, but the student model is trained on a strongly augmented version of the image. It's not contrastive learning because the objective is still explicitly object detection, but instead easy vs hard is the original image vs the strongly augmented one.