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Conference Paper
Domain Adaptive Detector via Variational Inference
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- Authors
- Hwa-Rang Kim, Kwang-Ju Kim, Doo-Hyun Choi
- Issue Date
- 2022-08
- Citation
- International Conference on Platform Technology and Service (PlatCon) 2022, pp.86-91
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Recent deep learning-based models work enough for object detection. However, there are still challenging tasks aiming to address domain shift problem caused when a model is applied to target data that have different domains from source data. In this paper, we are concerned with a scenario in domain generalization to aim to perform well on unseen target domains. We propose a novel single domain generalization method using variational inference to improve cross-domain robustness for object detection. We build a model that has latent features following a prior distribution to achieve feature alignment. Specifically, the latent features are guided to follow Gaussian distribution for arbitrary inputs, hence the model can be domain-invariant. We utilize Faster R-CNN as a base detector, and our proposed approach is evaluated on Cityscapes, KITTI, SIM10k datasets. The results show that our method can provides a general solution for cross-domain robustness, only using source data in tackling single domain generalization.
- KSP Keywords
- Cross Domain, Different domains, Faster r-cnn, Feature alignment, Gaussian Distribution, Learning-based, Object detection, Target data, Variational inference, adaptive detector, deep learning(DL)