{"title":"Metric-guided class-level alignment for domain adaptation","authors":"Xiaoshun Wang, Yunhan Li","doi":"10.1049/cvi2.12322","DOIUrl":null,"url":null,"abstract":"<p>The utilisation of domain adaptation methods facilitates the resolution of classification challenges in an unlabelled target domain by capitalising on the labelled information from source domains. Unfortunately, previous domain adaptation methods have focused mostly on global domain adaptation and have not taken into account class-specific data, which leads to poor knowledge transfer performance. The study of class-level domain adaptation, which aims to precisely match the distributions of different domains, has garnered attention in recent times. However, existing investigations into class-level alignment frequently align domain features either directly on or in close proximity to classification boundaries, resulting in the creation of uncertain samples that could potentially impair classification accuracy. To address the aforementioned problem, we propose a new approach called metric-guided class-level alignment (MCA) as a solution to this problem. Specifically, we employ different metrics to enable the network to acquire supplementary information, thereby enhancing class-level alignment. Moreover, MCA can be effectively combined with existing domain-level alignment methods to successfully mitigate the challenges posed by domain shift. Extensive testing on commonly-used public datasets shows that our method outperforms many other cutting-edge domain adaptation methods, showing significant gains over baseline performance.</p>","PeriodicalId":56304,"journal":{"name":"IET Computer Vision","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cvi2.12322","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Computer Vision","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cvi2.12322","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The utilisation of domain adaptation methods facilitates the resolution of classification challenges in an unlabelled target domain by capitalising on the labelled information from source domains. Unfortunately, previous domain adaptation methods have focused mostly on global domain adaptation and have not taken into account class-specific data, which leads to poor knowledge transfer performance. The study of class-level domain adaptation, which aims to precisely match the distributions of different domains, has garnered attention in recent times. However, existing investigations into class-level alignment frequently align domain features either directly on or in close proximity to classification boundaries, resulting in the creation of uncertain samples that could potentially impair classification accuracy. To address the aforementioned problem, we propose a new approach called metric-guided class-level alignment (MCA) as a solution to this problem. Specifically, we employ different metrics to enable the network to acquire supplementary information, thereby enhancing class-level alignment. Moreover, MCA can be effectively combined with existing domain-level alignment methods to successfully mitigate the challenges posed by domain shift. Extensive testing on commonly-used public datasets shows that our method outperforms many other cutting-edge domain adaptation methods, showing significant gains over baseline performance.
期刊介绍:
IET Computer Vision seeks original research papers in a wide range of areas of computer vision. The vision of the journal is to publish the highest quality research work that is relevant and topical to the field, but not forgetting those works that aim to introduce new horizons and set the agenda for future avenues of research in computer vision.
IET Computer Vision welcomes submissions on the following topics:
Biologically and perceptually motivated approaches to low level vision (feature detection, etc.);
Perceptual grouping and organisation
Representation, analysis and matching of 2D and 3D shape
Shape-from-X
Object recognition
Image understanding
Learning with visual inputs
Motion analysis and object tracking
Multiview scene analysis
Cognitive approaches in low, mid and high level vision
Control in visual systems
Colour, reflectance and light
Statistical and probabilistic models
Face and gesture
Surveillance
Biometrics and security
Robotics
Vehicle guidance
Automatic model aquisition
Medical image analysis and understanding
Aerial scene analysis and remote sensing
Deep learning models in computer vision
Both methodological and applications orientated papers are welcome.
Manuscripts submitted are expected to include a detailed and analytical review of the literature and state-of-the-art exposition of the original proposed research and its methodology, its thorough experimental evaluation, and last but not least, comparative evaluation against relevant and state-of-the-art methods. Submissions not abiding by these minimum requirements may be returned to authors without being sent to review.
Special Issues Current Call for Papers:
Computer Vision for Smart Cameras and Camera Networks - https://digital-library.theiet.org/files/IET_CVI_SC.pdf
Computer Vision for the Creative Industries - https://digital-library.theiet.org/files/IET_CVI_CVCI.pdf