Diffusing More Objects for Semi-Supervised Domain Adaption with Less Labeling
conference paper
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.
TNO Identifier
990467
Publisher
TNO
Source title
Neural Information Processing Systems (NeurIPS) 2023 Workshop on Diffusion Models.
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