Intrinsic feature extraction for unsupervised domain adaptation

IF 2.5 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Web Information Systems Pub Date : 2023-07-31 DOI:10.1108/ijwis-04-2023-0062
Xinzhi Cao, Yinsai Guo, Wenbin Yang, Xiangfeng Luo, Shaorong Xie
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引用次数: 2

Abstract

Purpose Unsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively. Design/methodology/approach IFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain. Findings Experimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets. Research limitations/implications Limitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared. Originality/value This paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.
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无监督域自适应的固有特征提取
目的无监督的领域自适应对象检测不仅可以减轻由于领域差距而导致的模型糟糕的性能,而且能够将在特定领域训练的知识应用到不同的领域。然而,对齐整个特征可能会混淆对象和背景信息,从而使提取判别特征变得具有挑战性。本文旨在提出一种改进的方法,称为内禀特征提取域自适应(IFEDA),以有效地提取判别特征。设计/方法论/方法IFEDA由内部特征提取(IFE)模块和对象一致性约束(OCC)组成。IFE模块是在实例层面设计的,主要解决了判别对象特征提取困难的问题。具体来说,可以更加关注对象的判别区域。同时,部署OCC来确定目标域中的类别预测是否与源域中的分类预测一致。实验结果证明了我们方法的有效性,并在具有挑战性的数据集上取得了良好的结果。研究局限性/含义本研究的局限性在于只应用了一个目标领域,当出现数据集不足或领域不可见的问题时,可能会改变模型的泛化能力。独创性/价值本文通过解决判别特征提取的困难,解决了关键信息缺陷的问题。为了更好地检测物体,两个领域中的类别必须一致。
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来源期刊
International Journal of Web Information Systems
International Journal of Web Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.60
自引率
0.00%
发文量
19
期刊介绍: The Global Information Infrastructure is a daily reality. In spite of the many applications in all domains of our societies: e-business, e-commerce, e-learning, e-science, and e-government, for instance, and in spite of the tremendous advances by engineers and scientists, the seamless development of Web information systems and services remains a major challenge. The journal examines how current shared vision for the future is one of semantically-rich information and service oriented architecture for global information systems. This vision is at the convergence of progress in technologies such as XML, Web services, RDF, OWL, of multimedia, multimodal, and multilingual information retrieval, and of distributed, mobile and ubiquitous computing. Topicality While the International Journal of Web Information Systems covers a broad range of topics, the journal welcomes papers that provide a perspective on all aspects of Web information systems: Web semantics and Web dynamics, Web mining and searching, Web databases and Web data integration, Web-based commerce and e-business, Web collaboration and distributed computing, Internet computing and networks, performance of Web applications, and Web multimedia services and Web-based education.
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