Progressive domain adaptation for object detection

Han Kai Hsu, Chun Han Yao, Yi Hsuan Tsai, Wei Chih Hung, Hung Yu Tseng, Maneesh Singh, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages738-746
Number of pages9
ISBN (Electronic)9781728165530
DOIs
Publication statusPublished - 2020 Mar
Event2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020 - Snowmass Village, United States
Duration: 2020 Mar 12020 Mar 5

Publication series

NameProceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020

Conference

Conference2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020
CountryUnited States
CitySnowmass Village
Period20/3/120/3/5

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Hsu, H. K., Yao, C. H., Tsai, Y. H., Hung, W. C., Tseng, H. Y., Singh, M., & Yang, M. H. (2020). Progressive domain adaptation for object detection. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 738-746). [9093358] (Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV45572.2020.9093358