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Spatial Attention Pyramid Network for Unsupervised Domain Adaptation

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12358)

Abstract

Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, and semantic segmentation, which aims to alleviate performance degradation caused by domain-shift. Most of previous methods rely on a single-mode distribution of source and target domains to align them with adversarial learning, leading to inferior results in various scenarios. To that end, in this paper, we design a new spatial attention pyramid network for unsupervised domain adaptation. Specifically, we first build the spatial pyramid representation to capture context information of objects at different scales. Guided by the task-specific information, we combine the dense global structure representation and local texture patterns at each spatial location effectively using the spatial attention mechanism. In this way, the network is enforced to focus on the discriminative regions with context information for domain adaptation. We conduct extensive experiments on various challenging datasets for unsupervised domain adaptation on object detection, instance segmentation, and semantic segmentation, which demonstrates that our method performs favorably against the state-of-the-art methods by a large margin. Our source code is available at https://isrc.iscas.ac.cn/gitlab/research/domain-adaption.

Keywords

Unsupervised domain adaptation Spatial attention pyramid Object detection Semantic segmentation Instance segmentation 

Notes

Acknowledgement

This work was supported by the Key Research Program of Frontier Sciences, CAS, Grant No. ZDBS-LY-JSC038, the National Natural Science Foundation of China, Grant No. 61807033 and National Key Research and Development Program of China (2017YFB0801900). Libo Zhang was supported by Youth Innovation Promotion Association, CAS (2020111), and Outstanding Youth Scientist Project of ISCAS.

Supplementary material

504454_1_En_29_MOESM1_ESM.zip (12.6 mb)
Supplementary material 1 (zip 12909 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.University at Albany, State University of New YorkAlbanyUSA
  3. 3.State Key Laboratory of Computer Science, Institute of Software Chinese Academy of SciencesBeijingChina
  4. 4.JD Finance America Corporation, Mountain ViewCAUSA
  5. 5.Tianjin UniversityTianjinChina

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