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Simulating Content Consistent Vehicle Datasets with Attribute Descent

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

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Abstract

This paper uses a graphic engine to simulate a large amount of training data with free annotations. Between synthetic and real data, there is a two-level domain gap, i.e., content level and appearance level. While the latter has been widely studied, we focus on reducing the content gap in attributes like illumination and viewpoint. To reduce the problem complexity, we choose a smaller and more controllable application, vehicle re-identification (re-ID). We introduce a large-scale synthetic dataset VehicleX. Created in Unity, it contains 1,362 vehicles of various 3D models with fully editable attributes. We propose an attribute descent approach to let VehicleX approximate the attributes in real-world datasets. Specifically, we manipulate each attribute in VehicleX, aiming to minimize the discrepancy between VehicleX and real data in terms of the Fréchet Inception Distance (FID). This attribute descent algorithm allows content domain adaptation (DA) orthogonal to existing appearance DA methods. We mix the optimized VehicleX data with real-world vehicle re-ID datasets, and observe consistent improvement. With the augmented datasets, we report competitive accuracy. We make the dataset, engine and our codes available at https://github.com/yorkeyao/VehicleX.

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Notes

  1. 1.

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Acknowledgement

Dr. Liang Zheng is the recipient of Australian Research Council Discovery Early Career Award (DE200101283) funded by the Australian Government.

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Authors and Affiliations

  1. Australian National University, Canberra, Australia

    Yue Yao, Liang Zheng & Tom Gedeon

  2. NVIDIA, Santa Clara, USA

    Xiaodong Yang & Milind Naphade

Authors
  1. Yue Yao
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  2. Liang Zheng
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  3. Xiaodong Yang
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  4. Milind Naphade
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  5. Tom Gedeon
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Corresponding author

Correspondence to Liang Zheng .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Yao, Y., Zheng, L., Yang, X., Naphade, M., Gedeon, T. (2020). Simulating Content Consistent Vehicle Datasets with Attribute Descent. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_46

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_46

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  • Print ISBN: 978-3-030-58538-9

  • Online ISBN: 978-3-030-58539-6

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