Abstract
Cross-domain object detection usually solves the problem of domain transfer by reducing the difference between the source domain and target domain. However, existing solutions do not effectively solve the performance degradation caused by cross-domain differences. To address this problem, we present the Cross-domain Object Detection Model via Contrastive Learning with Style Transfer(COCS). Our model is based on generating new samples with source domain information and target domain style. In addition, the importance of new samples feature information are aimed to match positive and negative samples for comparative learning better. So, we transfer source domain with labeled to get new samples with style of target domain. Then we employ momentum contrast learning method to maximize the similarly between positive sample pairs representations and minimize the loss function. Moreover, our model can be adapted to different style domains, which further expands the application scenarios. Experiments on a benchmark dataset demonstrate that our model achieves or matches the state-of-the-art approaches.
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Acknowledgements
This work was supported by Joint Fund of Natural Science Foundation of Anhui Province in 2020 (2008085UD08), Anhui Provincial Key R &D Program (202004a05020004), Open fund of Intelligent Interconnected Systems Laboratory of Anhui Province (PA2021AKSK0107), Intelligent Networking and New Energy Vehicle Special Project of Intelligent Manufacturing Institute of HFUT (IMIWL2019003, IMIDC2019002).
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Zhao, M. et al. (2023). Cross-domain Object Detection Model via Contrastive Learning with Style Transfer. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1793. Springer, Singapore. https://doi.org/10.1007/978-981-99-1645-0_34
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DOI: https://doi.org/10.1007/978-981-99-1645-0_34
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