The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
감독되지 않은 도메인 적응(DA)은 레이블이 지정된 훈련(소스)과 레이블이 지정되지 않은 테스트(대상) 세트가 서로 다른 도메인에 속하고 서로 다른 특징 분포를 갖기 때문에 어려운 기계 학습 문제입니다. 이는 최근 미세 표현 인식(MER)에서 폭넓은 관심을 끌었습니다. ). 잘 수행되는 비지도 DA 방법이 일부 제안되었지만 이러한 방법은 MER, 즉 교차 도메인 MER의 비지도 DA 문제를 잘 해결할 수 없습니다. 이러한 어려운 문제를 해결하기 위해 이 편지에서 우리는 JPMM(Joint Patch Weighting and Moment Matching)이라는 새로운 비지도 DA 방법을 제안합니다. JPMM은 다중 차수 순간 일치 작업을 통해 확률 분포 발산을 최소화하여 소스 및 대상 미세 표현 기능 세트를 연결합니다. 한편, 가중치 학습을 통해 기여하는 얼굴 패치를 활용하여 미세 표정 식별 정보가 포함된 도메인 불변 특징 표현을 학습할 수 있습니다. 마지막으로, 우리는 제안된 JPMM 방법이 교차 도메인 MER을 처리하는 데 있어 최근 최첨단 비지도 DA 방법보다 우수하다는 것을 평가하기 위해 광범위한 실험을 수행합니다.
Jie ZHU
Southeast University
Yuan ZONG
Southeast University
Hongli CHANG
Southeast University
Li ZHAO
Southeast University
Chuangao TANG
Southeast University
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부
Jie ZHU, Yuan ZONG, Hongli CHANG, Li ZHAO, Chuangao TANG, "Joint Patch Weighting and Moment Matching for Unsupervised Domain Adaptation in Micro-Expression Recognition" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 2, pp. 441-445, February 2022, doi: 10.1587/transinf.2021EDL8045.
Abstract: Unsupervised domain adaptation (DA) is a challenging machine learning problem since the labeled training (source) and unlabeled testing (target) sets belong to different domains and then have different feature distributions, which has recently attracted wide attention in micro-expression recognition (MER). Although some well-performing unsupervised DA methods have been proposed, these methods cannot well solve the problem of unsupervised DA in MER, a. k. a., cross-domain MER. To deal with such a challenging problem, in this letter we propose a novel unsupervised DA method called Joint Patch weighting and Moment Matching (JPMM). JPMM bridges the source and target micro-expression feature sets by minimizing their probability distribution divergence with a multi-order moment matching operation. Meanwhile, it takes advantage of the contributive facial patches by the weight learning such that a domain-invariant feature representation involving micro-expression distinguishable information can be learned. Finally, we carry out extensive experiments to evaluate the proposed JPMM method is superior to recent state-of-the-art unsupervised DA methods in dealing with cross-domain MER.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8045/_p
부
@ARTICLE{e105-d_2_441,
author={Jie ZHU, Yuan ZONG, Hongli CHANG, Li ZHAO, Chuangao TANG, },
journal={IEICE TRANSACTIONS on Information},
title={Joint Patch Weighting and Moment Matching for Unsupervised Domain Adaptation in Micro-Expression Recognition},
year={2022},
volume={E105-D},
number={2},
pages={441-445},
abstract={Unsupervised domain adaptation (DA) is a challenging machine learning problem since the labeled training (source) and unlabeled testing (target) sets belong to different domains and then have different feature distributions, which has recently attracted wide attention in micro-expression recognition (MER). Although some well-performing unsupervised DA methods have been proposed, these methods cannot well solve the problem of unsupervised DA in MER, a. k. a., cross-domain MER. To deal with such a challenging problem, in this letter we propose a novel unsupervised DA method called Joint Patch weighting and Moment Matching (JPMM). JPMM bridges the source and target micro-expression feature sets by minimizing their probability distribution divergence with a multi-order moment matching operation. Meanwhile, it takes advantage of the contributive facial patches by the weight learning such that a domain-invariant feature representation involving micro-expression distinguishable information can be learned. Finally, we carry out extensive experiments to evaluate the proposed JPMM method is superior to recent state-of-the-art unsupervised DA methods in dealing with cross-domain MER.},
keywords={},
doi={10.1587/transinf.2021EDL8045},
ISSN={1745-1361},
month={February},}
부
TY - JOUR
TI - Joint Patch Weighting and Moment Matching for Unsupervised Domain Adaptation in Micro-Expression Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 441
EP - 445
AU - Jie ZHU
AU - Yuan ZONG
AU - Hongli CHANG
AU - Li ZHAO
AU - Chuangao TANG
PY - 2022
DO - 10.1587/transinf.2021EDL8045
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2022
AB - Unsupervised domain adaptation (DA) is a challenging machine learning problem since the labeled training (source) and unlabeled testing (target) sets belong to different domains and then have different feature distributions, which has recently attracted wide attention in micro-expression recognition (MER). Although some well-performing unsupervised DA methods have been proposed, these methods cannot well solve the problem of unsupervised DA in MER, a. k. a., cross-domain MER. To deal with such a challenging problem, in this letter we propose a novel unsupervised DA method called Joint Patch weighting and Moment Matching (JPMM). JPMM bridges the source and target micro-expression feature sets by minimizing their probability distribution divergence with a multi-order moment matching operation. Meanwhile, it takes advantage of the contributive facial patches by the weight learning such that a domain-invariant feature representation involving micro-expression distinguishable information can be learned. Finally, we carry out extensive experiments to evaluate the proposed JPMM method is superior to recent state-of-the-art unsupervised DA methods in dealing with cross-domain MER.
ER -