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Six-Channel Image Representation for Cross-Domain Object Detection

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Image and Graphics (ICIG 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12888))

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Abstract

Most deep learning models are data-driven and the excellent performance is highly dependent on the abundant and diverse datasets. However, it is very hard to obtain and label the datasets of some specific scenes or applications. If we train the detector using the data from one domain, it cannot perform well on the data from another domain due to domain shift, which is one of the big challenges of most object detection models. To address this issue, some image-to-image translation techniques have been employed to generate some fake data of some specific scenes to train the models. With the advent of Generative Adversarial Networks (GANs), we could realize unsupervised image-to-image translation in both directions from a source to a target domain and from the target to the source domain. In this study, we report a new approach to making use of the generated images. We propose to concatenate the original 3-channel images and their corresponding GAN-generated fake images to form 6-channel representations of the dataset, ho** to address the domain shift problem while exploiting the success of available detection models. The idea of augmented data representation may inspire further study on object detection and other applications.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Tianxiao Zhang & Wenchi Ma

  2. Department of Computer Science, Ryerson University, Toronto, ON, M5B 2K3, Canada

    Guanghui Wang

Authors
  1. Tianxiao Zhang
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  2. Wenchi Ma
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  3. Guanghui Wang
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Corresponding author

Correspondence to Guanghui Wang .

Editor information

Editors and Affiliations

  1. Peking University, Bei**g, China

    Yuxin Peng

  2. Tsinghua University, Bei**g, China

    Shi-Min Hu

  3. Tampere University, Tampere, Finland

    Moncef Gabbouj

  4. Zhejiang University, Hangzhou, China

    Kun Zhou

  5. Technion – Israel Institute of Technology, Haifa, Israel

    Michael Elad

  6. Tsinghua University, Bei**g, China

    Kun Xu

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Zhang, T., Ma, W., Wang, G. (2021). Six-Channel Image Representation for Cross-Domain Object Detection. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-87355-4_15

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