Diffusing More Objects for Semi-Supervised Domain Adaptation with Less
  Labeling
Leander van den Heuvel Gertjan Burghouts David W. Zhang Gwenn Englebienne Sabina B. van Rooij
For object detection, it is possible to view the prediction of bounding boxes
as a reverse diffusion process. Using a diffusion model, the random bounding
boxes are iteratively refined in a denoising step, conditioned on the image. We
propose a stochastic accumulator function that starts each run with random
bounding boxes and combines the slightly different predictions. We empirically
verify that this improves detection performance. The improved detections are
leveraged on unlabelled images as weighted pseudo-labels for semi-supervised
learning. We evaluate the method on a challenging out-of-domain test set. Our
method brings significant improvements and is on par with human-selected
pseudo-labels, while not requiring any human involvement.