Abstract
Generating models to handle new visual tasks requires additional datasets, which take considerable effort to create. We propose a method of domain adaptation for merging multiple models with less effort than creating an additional dataset. This method merges pre-trained models in different domains using glue layers and a generative model, which feeds latent features to the glue layers to train them without an additional dataset. We also propose a generative model that is created by distilling knowledge from pre-trained models. This enables the dataset to be reused to create latent features for training the glue layers. We apply this method to object detection in a low-light situation. The YOLO-in-the-Dark model comprises two models, Learning-to-See-in-the-Dark model and YOLO. We present the proposed method and report the result of domain adaptation to detect objects from RAW short-exposure low-light images. The YOLO-in-the-Dark model uses fewer computing resources than the naive approach.
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References
Chang, W.G., You, T., Seo, S., Kwak, S., Han, B.: Domain-specific batch normalization for unsupervised domain adaptation. In: CVPR 2019 (2019)
Chen, C., Chen, Q., Xu, J., Koltun, V.: Learning to see in the dark. In: CVPR 2018 (2018)
Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. In: Advances in Neural Information Processing Systems 30, pp. 742–751 (2017)
Everingham, M., Gool, L.V., Williams, C., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2012 (VOC2012) results. In: VOC 2012 (2012)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015). arXiv:1503.02531
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances Neural Information Processing Systems 25 (NIPS), pp. 1097–1105 (2012)
Lin, T.Y., et al.: Microsoft coco: common objects in context (2014). arXiv:1405.0312
Loh, Y.P., Chan, C.S.: Getting to know low-light images with the exclusively dark dataset. Comput. Vis. Image Underst. 178, 30–42 (2019). https://doi.org/10.1016/j.cviu.2018.10.010
Luo, J., Xu, Y., Tang, C., Lv, J.: Learning inverse mapping by autoencoder based generative adversarial nets (2017). arXiv:1703.10094
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR 2016 (2016)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). arXiv:1804.02767
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets (2014). arXiv:1412.6550
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI (2015)
Teng, Y., Choromanska, A., Bojarski, M.: Invertible autoencoder for domain adaptation (2018). arXiv:1802.06869
Xie, S., Zheng, Z., Chen, L., Chen, C.: Learning semantic representations for unsupervised domain adaptation. In: ICML 2018 (2018)
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Sasagawa, Y., Nagahara, H. (2020). YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12366. Springer, Cham. https://doi.org/10.1007/978-3-030-58589-1_21
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DOI: https://doi.org/10.1007/978-3-030-58589-1_21
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