Deeply Aligned Adaptation for Cross-domain Object Detection
- URL: http://arxiv.org/abs/2004.02093v2
- Date: Thu, 9 Apr 2020 01:39:11 GMT
- Title: Deeply Aligned Adaptation for Cross-domain Object Detection
- Authors: Minghao Fu, Zhenshan Xie, Wen Li, Lixin Duan
- Abstract summary: Cross-domain object detection has recently attracted more and more attention for real-world applications.
We propose an end-to-end solution based on Faster R-CNN, where ground-truth annotations are available for source images but not for target ones.
- Score: 33.766468227676214
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cross-domain object detection has recently attracted more and more attention
for real-world applications, since it helps build robust detectors adapting
well to new environments. In this work, we propose an end-to-end solution based
on Faster R-CNN, where ground-truth annotations are available for source images
(e.g., cartoon) but not for target ones (e.g., watercolor) during training.
Motivated by the observation that the transferabilities of different neural
network layers differ from each other, we propose to apply a number of domain
alignment strategies to different layers of Faster R-CNN, where the alignment
strength is gradually reduced from low to higher layers. Moreover, after
obtaining region proposals in our network, we develop a foreground-background
aware alignment module to further reduce the domain mismatch by separately
aligning features of the foreground and background regions from the source and
target domains. Extensive experiments on benchmark datasets demonstrate the
effectiveness of our proposed approach.
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