Cross-Domain Object Detection Using Unsupervised Image Translation

• URL: http://arxiv.org/abs/2601.11779v1
• Date: Fri, 16 Jan 2026 21:02:42 GMT
• Title: Cross-Domain Object Detection Using Unsupervised Image Translation
• Authors: Vinicius F. Arruda, Rodrigo F. Berriel, Thiago M. Paixão, Claudine Badue, Alberto F. De Souza, Nicu Sebe, Thiago Oliveira-Santos,
• Abstract summary: We propose a method to generate an artificial dataset in the target domain to train an object detector.<n>Our key contributions are the proposal of a less complex yet more effective method that also has an improved interpretability.
• Score: 39.857749360034916
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised domain adaptation for object detection addresses the adaption of detectors trained in a source domain to work accurately in an unseen target domain. Recently, methods approaching the alignment of the intermediate features proven to be promising, achieving state-of-the-art results. However, these methods are laborious to implement and hard to interpret. Although promising, there is still room for improvements to close the performance gap toward the upper-bound (when training with the target data). In this work, we propose a method to generate an artificial dataset in the target domain to train an object detector. We employed two unsupervised image translators (CycleGAN and an AdaIN-based model) using only annotated data from the source domain and non-annotated data from the target domain. Our key contributions are the proposal of a less complex yet more effective method that also has an improved interpretability. Results on real-world scenarios for autonomous driving show significant improvements, outperforming state-of-the-art methods in most cases, further closing the gap toward the upper-bound.

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