research-article

Dual Adversarial Network for Unsupervised Ground/Satellite-to-Aerial Scene Adaptation

Authors:

Z. Jane WangAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 10 - 18

https://doi.org/10.1145/3394171.3413893

Published: 12 October 2020 Publication History

Abstract

Recent domain adaptation work tends to obtain a uniformed representation in an adversarial manner through joint learning of the domain discriminator and feature generator. However, this domain adversarial approach could render sub-optimal performances due to two potential reasons: First, it might fail to consider the task at hand when matching the distributions between the domains. Second, it generally treats the source and target domain data in the same way. In our opinion, the source domain data which serves the feature adaption purpose should be supplementary, whereas the target domain data mainly needs to consider the task-specific classifier. Motivated by this, we propose a dual adversarial network for domain adaptation, where two adversarial learning processes are conducted iteratively, in correspondence with the feature adaptation and the classification task respectively. The efficacy of the proposed method is first demonstrated on Visual Domain Adaptation Challenge (VisDA) 2017 challenge, and then on two newly proposed Ground/Satellite-to-Aerial Scene adaptation tasks. For the proposed tasks, the data for the same scene is collected not only by the traditional camera on the ground, but also by satellite from the out space and unmanned aerial vehicle (UAV) at the high-altitude. Since the semantic gap between the ground/satellite scene and the aerial scene is much larger than that between ground scenes, the newly proposed tasks are more challenging than traditional domain adaptation tasks. The datasets/codes can be found at https://github.com/jianzhelin/DuAN.

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Zhang HShen HYuan QGuan X(2022)Multispectral and SAR Image Fusion Based on Laplacian Pyramid and Sparse RepresentationRemote Sensing10.3390/rs1404087014:4(870)Online publication date: 11-Feb-2022
https://doi.org/10.3390/rs14040870
Sun JShen XSun Q(2022)Hyperspectral Image Few-Shot Classification Network Based on the Earth Mover’s DistanceIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2022.319154160(1-14)Online publication date: 2022
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Index Terms

Dual Adversarial Network for Unsupervised Ground/Satellite-to-Aerial Scene Adaptation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

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October 12 - 16, 2020

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Zhang JLi WSun WZhang YTao R(2024)Locality Robust Domain Adaptation for cross-scene hyperspectral image classificationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121822238:PAOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121822
Zhang HShen HYuan QGuan X(2022)Multispectral and SAR Image Fusion Based on Laplacian Pyramid and Sparse RepresentationRemote Sensing10.3390/rs1404087014:4(870)Online publication date: 11-Feb-2022
https://doi.org/10.3390/rs14040870
Sun JShen XSun Q(2022)Hyperspectral Image Few-Shot Classification Network Based on the Earth Mover’s DistanceIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2022.319154160(1-14)Online publication date: 2022
https://doi.org/10.1109/TGRS.2022.3191541
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