Recent deep learning methods for object detection rely on a large amount of bounding box annotations. Collecting these annotations is laborious and costly, yet supervised models do not generalize well when testing on images from a different distribution. Domain adaptation provides a solution by adapting existing labels to the target testing data. However, a large gap between domains could make adaptation a challenging task, which leads to unstable training processes and sub-optimal results. In this paper, we propose to bridge the domain gap with an intermediate domain and progressively solve easier adaptation subtasks. This intermediate domain is constructed by translating the source images to mimic the ones in the target domain. To tackle the domain-shift problem, we adopt adversarial learning to align distributions at the feature level. In addition, a weighted task loss is applied to deal with unbalanced image quality in the intermediate domain. Experimental results show that our method performs favorably against the state-of-the-art method in terms of the performance on the target domain.
|Title of host publication||Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||9|
|Publication status||Published - 2020 Mar|
|Event||2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States|
Duration: 2020 Mar 1 → 2020 Mar 5
|Name||Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020|
|Conference||2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020|
|Period||20/3/1 → 20/3/5|
Bibliographical noteFunding Information:
Acknowledgement. This work is supported in part by the NSF CAREER Grant 1149783, gifts from Adobe, Verisk, and NEC.
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Vision and Pattern Recognition