{"title":"通过基于图神经网络的特征聚合技术进行少量缺陷分类","authors":"Pengcheng Zhang, Peixiao Zheng, Xin Guo, Enqing Chen","doi":"10.1016/j.jvcir.2024.104172","DOIUrl":null,"url":null,"abstract":"<div><p>The effectiveness of deep learning models is greatly dependent on the availability of a vast amount of labeled data. However, in the realm of surface defect classification, acquiring and annotating defect samples proves to be quite challenging. Consequently, accurately predicting defect types with only a limited number of labeled samples has emerged as a prominent research focus in recent years. Few-shot learning, which leverages a restricted sample set in the support set, can effectively predict the categories of unlabeled samples in the query set. This approach is particularly well-suited for defect classification scenarios. In this article, we propose a transductive few-shot surface defect classification method, which using both the instance-level relations and distribution-level relations in each few-shot learning task. Furthermore, we calculate class center features in transductive manner and incorporate them into the feature aggregation operation to rectify the positioning of edge samples in the mapping space. This adjustment aims to minimize the distance between samples of the same category, thereby mitigating the influence of unlabeled samples at category boundary on classification accuracy. Experimental results on the public dataset show the outstanding performance of our proposed approach compared to the state-of-the-art methods in the few-shot learning settings. Our code is available at <span>https://github.com/Harry10459/CIDnet</span><svg><path></path></svg>.</p></div>","PeriodicalId":54755,"journal":{"name":"Journal of Visual Communication and Image Representation","volume":null,"pages":null},"PeriodicalIF":2.6000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Few-shot defect classification via feature aggregation based on graph neural network\",\"authors\":\"Pengcheng Zhang, Peixiao Zheng, Xin Guo, Enqing Chen\",\"doi\":\"10.1016/j.jvcir.2024.104172\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The effectiveness of deep learning models is greatly dependent on the availability of a vast amount of labeled data. However, in the realm of surface defect classification, acquiring and annotating defect samples proves to be quite challenging. Consequently, accurately predicting defect types with only a limited number of labeled samples has emerged as a prominent research focus in recent years. Few-shot learning, which leverages a restricted sample set in the support set, can effectively predict the categories of unlabeled samples in the query set. This approach is particularly well-suited for defect classification scenarios. In this article, we propose a transductive few-shot surface defect classification method, which using both the instance-level relations and distribution-level relations in each few-shot learning task. Furthermore, we calculate class center features in transductive manner and incorporate them into the feature aggregation operation to rectify the positioning of edge samples in the mapping space. This adjustment aims to minimize the distance between samples of the same category, thereby mitigating the influence of unlabeled samples at category boundary on classification accuracy. Experimental results on the public dataset show the outstanding performance of our proposed approach compared to the state-of-the-art methods in the few-shot learning settings. Our code is available at <span>https://github.com/Harry10459/CIDnet</span><svg><path></path></svg>.</p></div>\",\"PeriodicalId\":54755,\"journal\":{\"name\":\"Journal of Visual Communication and Image Representation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Visual Communication and Image Representation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1047320324001275\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Visual Communication and Image Representation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1047320324001275","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Few-shot defect classification via feature aggregation based on graph neural network
The effectiveness of deep learning models is greatly dependent on the availability of a vast amount of labeled data. However, in the realm of surface defect classification, acquiring and annotating defect samples proves to be quite challenging. Consequently, accurately predicting defect types with only a limited number of labeled samples has emerged as a prominent research focus in recent years. Few-shot learning, which leverages a restricted sample set in the support set, can effectively predict the categories of unlabeled samples in the query set. This approach is particularly well-suited for defect classification scenarios. In this article, we propose a transductive few-shot surface defect classification method, which using both the instance-level relations and distribution-level relations in each few-shot learning task. Furthermore, we calculate class center features in transductive manner and incorporate them into the feature aggregation operation to rectify the positioning of edge samples in the mapping space. This adjustment aims to minimize the distance between samples of the same category, thereby mitigating the influence of unlabeled samples at category boundary on classification accuracy. Experimental results on the public dataset show the outstanding performance of our proposed approach compared to the state-of-the-art methods in the few-shot learning settings. Our code is available at https://github.com/Harry10459/CIDnet.
期刊介绍:
The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.