结合自集成和对抗学习的域自适应城市场景语义分割

doi:10.16451/j.cnki.issn1003-6059.202101006

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Abstract Aiming at the problem of high cost of urban scene label acquisition, an algorithm of domain adaptation semantic segmentation for urban scene combining self-ensembling and adversarial learning is proposed. For the inter-domain gap between source and target domains, the method of style transfer is employed to transfer the source domain into a new dataset with the style of target domain. For the problem of intra-domain gap in the target domain, the self-ensembling method is introduced and a teacher network is constructed. The teacher network is utilized to supervise and guide the student network through consistency constraints on the target domain segmentation map to reduce the intra-domain gap of the target domain and improve the segmentation accuracy. The self-training method is exploited to obtain the pseudo label of the target domain and add the pseudo label into the adversarial learning method to retrain the network and further improve the segmentation ability. Experiments on segmentation datasets verify the effectiveness of the proposed algorithm.

Key words： Self-ensembling Adversarial Learning Domain Adaptation Urban Scene Semantic Segmentation

Received: 28 September 2020

ZTFLH:

TP 319.4

Fund:National Natural Science Foundation of China(No.61462065,61661036)

Corresponding Authors: ZHANG Guimei, Ph.D., professor. Her research interests include computer vision, image processing and pattern recognition.

About author:: LU Feifei, master student. His research interests include computer vision, image processing and pattern recognition.
LONG Bangyao, master student. His research
interests include computer vision, image processing and pattern recognition.
MIAO Jun, Ph.D., professor. His research interests include image based 3D reconstruction, image processing and pattern recognition.

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	ZHANG Guimei
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	LONG Bangyao
	MIAO Jun

Cite this article:

ZHANG Guimei,LU Feifei,LONG Bangyao等. Domain Adaptation Semantic Segmentation for Urban Scene Combining Self-ensembling and Adversarial Learning[J]. , 2021, 34(1): 58-67.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202101006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I1/58

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