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Pixel-level intra-domain adaptation for semantic segmentation

Yan, Zizheng, Yu, Xianggang, Qin, Yipeng ORCID:, Wu, Yushuang, Han, Xiaoguang and Cui, Shuguang 2021. Pixel-level intra-domain adaptation for semantic segmentation. Presented at: ACM Multimedia 2021, Chengdu, China, 20-24 October 2021. MM '21: Proceedings of the 29th ACM International Conference on Multimedia. ACM, pp. 404-413. 10.1145/3474085.3475174

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Recent advances in unsupervised domain adaptation have achieved remarkable performance on semantic segmentation tasks. Despite such progress, existing works mainly focus on bridging the inter-domain gaps between the source and target domain, while only few of them noticed the intra-domain gaps within the target data. In this work, we propose a pixel-level intra-domain adaptation approach to reduce the intra-domain gaps within the target data. Compared with image-level methods, ours treats each pixel as an instance, which adapts the segmentation model at a more fine-grained level. Specifically, we first conduct the inter-domain adaptation between the source and target domain; Then, we separate the pixels in target images into the easy and hard subdomains; Finally, we propose a pixel-level adversarial training strategy to adapt a segmentation network from the easy to the hard subdomain. Moreover, we show that the segmentation accuracy can be further improved by incorporating a continuous indexing technique in the adversarial training. Experimental results show the effectiveness of our method against existing state-of-the-art approaches.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: ACM
Date of First Compliant Deposit: 9 August 2021
Date of Acceptance: 4 July 2021
Last Modified: 09 Nov 2022 11:25

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