ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-Based Mixing

Giulio Mattolin, Luca Zanella, Elisa Ricci, Yiming Wang; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023, pp. 423-433

Abstract


Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detection confidence for adaptive object detector learning. We mix the local region of the target sample that corresponds to the most confident pseudo detections with a source image, and apply an additional consistency loss term to gradually adapt towards the target data distribution. In order to robustly define a confidence score for a region, we exploit the confidence score per pseudo detection that accounts for both the detector-dependent confidence and the bounding box uncertainty. Moreover, we propose a novel pseudo labelling scheme that progressively filters the pseudo target detections using the confidence metric that varies from a loose to strict manner along the training. We perform extensive experiments with three datasets, achieving state-of-the-art performance in two of them and approaching the supervised target model performance in the other. Code is available at https://github.com/giuliomattolin/ConfMix.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Mattolin_2023_WACV, author = {Mattolin, Giulio and Zanella, Luca and Ricci, Elisa and Wang, Yiming}, title = {ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-Based Mixing}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2023}, pages = {423-433} }