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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Si Liu

Institute of Information Engineering, Chinese Academy of Sciences


Yao Sun

Institute of Information Engineering, Chinese Academy of Sciences


Defa Zhu

Institute of Information Engineering, Chinese Academy of Sciences


Guanghui Ren

Institute of Information Engineering, Chinese Academy of Sciences


Yu Chen

JD.com


Jiashi Feng

National University of Singapore


Jizhong Han

Institute of Information Engineering, Chinese Academy of Sciences


DOI:

10.1609/aaai.v32i1.12320


Abstract:

Human parsing has been extensively studied recently due to its wide applications in many important scenarios. Mainstream fashion parsing models (i.e., parsers) focus on parsing the high-resolution and clean images. However, directly applying the parsers trained on benchmarks of high-quality samples to a particular application scenario in the wild, e.g., a canteen, airport or workplace, often gives non-satisfactory performance due to domain shift. In this paper, we explore a new and challenging cross-domain human parsing problem: taking the benchmark dataset with extensive pixel-wise labeling as the source domain, how to obtain a satisfactory parser on a new target domain without requiring any additional manual labeling? To this end, we propose a novel and efficient cross-domain human parsing model to bridge the cross-domain differences in terms of visual appearance and environment conditions and fully exploit commonalities across domains. Our proposed model explicitly learns a feature compensation network, which is specialized for mitigating the cross-domain differences. A discriminative feature adversarial network is introduced to supervise the feature compensation to effectively reduces the discrepancy between feature distributions of two domains. Besides, our proposed model also introduces a structured label adversarial network to guide the parsing results of the target domain to follow the high-order relationships of the structured labels shared across domains. The proposed framework is end-to-end trainable, practical and scalable in real applications. Extensive experiments are conducted where LIP dataset is the source domain and 4 different datasets including surveillance videos, movies and runway shows without any annotations, are evaluated as target domains. The results consistently confirm data efficiency and performance advantages of the proposed method for the challenging cross-domain human parsing problem.

Topics: AAAI

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HOW TO CITE:

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation AAAI 2018, .

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han (2018). Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han. Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han. 2018. Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han. (2018) "Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han, "Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation", AAAI, p., 2018.

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han. "Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han. "Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Si Liu||Yao Sun||Defa Zhu||Guanghui Ren||Yu Chen||Jiashi Feng||Jizhong Han. Cross-Domain Human Parsing via Adversarial Feature and Label Adaptation. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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