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.
Cross-domain object detection using unsupervised image translation / Arruda, V. F.; Berriel, R. F.; Paixao, T. M.; Badue, C.; De Souza, A. F.; Sebe, N.; Oliveira-Santos, T.. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 192:(2022), p. 116334. [10.1016/j.eswa.2021.116334]
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Titolo: | Cross-domain object detection using unsupervised image translation | |
Autori: | Arruda, V. F.; Berriel, R. F.; Paixao, T. M.; Badue, C.; De Souza, A. F.; Sebe, N.; Oliveira-Santos, T. | |
Autori Unitn: | ||
Titolo del periodico: | EXPERT SYSTEMS WITH APPLICATIONS | |
Anno di pubblicazione: | 2022 | |
Codice identificativo Scopus: | 2-s2.0-85121911420 | |
Codice identificativo WOS: | WOS:000740741100001 | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.eswa.2021.116334 | |
Handle: | http://hdl.handle.net/11572/340560 | |
Citazione: | Cross-domain object detection using unsupervised image translation / Arruda, V. F.; Berriel, R. F.; Paixao, T. M.; Badue, C.; De Souza, A. F.; Sebe, N.; Oliveira-Santos, T.. - In: EXPERT SYSTEMS WITH APPLICATIONS. - ISSN 0957-4174. - 192:(2022), p. 116334. [10.1016/j.eswa.2021.116334] | |
Appare nelle tipologie: | 03.1 Articolo su rivista (Journal article) |