{"title":"利用胶囊注意力进行超低分辨率图像分类的深度混合架构","authors":"Hasindu Dewasurendra;Taejoon Kim","doi":"10.1109/ACCESS.2024.3469155","DOIUrl":null,"url":null,"abstract":"Despite extensive applications in surveillance and remote sensing, research on very low-resolution (VLR) image classification remains relatively unexplored in comparison to high-resolution (HR) image classification. We introduce a deep hybrid network that integrates capsule routing networks with a two-layer attention module. In the proposed architecture, the attention mechanism captures the more salient features, and the capsule network encodes these features to be robust to resolution changes. To enhance the network’s performance, a transfer learning on a custom image dataset, which is well-aligned to CIFAR-10, is utilized. The proposed model (Codes for the models are available at: \n<uri>https://github.com/kdhasi/Deep-CapsuleAttention.git</uri>\n) is evaluated on two VLR classification tasks of ‘VLR complex image’ and ‘VLR real-world digit’. Experimental results demonstrate the superiority of the proposed model, achieving state-of-the-art (SOTA) results in both VLR complex image and VLR real-world digit domains while using fewer parameters compared to previous SOTA networks. Specifically, on the VLR CIFAR-10 dataset, the proposed model attains a 3.17% improvement in detection accuracy over the current benchmarks, and, on the VLR SVHN dataset, it achieves a 3.85% improvement by using 80% fewer parameters.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10697178","citationCount":"0","resultStr":"{\"title\":\"Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule Attention\",\"authors\":\"Hasindu Dewasurendra;Taejoon Kim\",\"doi\":\"10.1109/ACCESS.2024.3469155\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Despite extensive applications in surveillance and remote sensing, research on very low-resolution (VLR) image classification remains relatively unexplored in comparison to high-resolution (HR) image classification. We introduce a deep hybrid network that integrates capsule routing networks with a two-layer attention module. In the proposed architecture, the attention mechanism captures the more salient features, and the capsule network encodes these features to be robust to resolution changes. To enhance the network’s performance, a transfer learning on a custom image dataset, which is well-aligned to CIFAR-10, is utilized. The proposed model (Codes for the models are available at: \\n<uri>https://github.com/kdhasi/Deep-CapsuleAttention.git</uri>\\n) is evaluated on two VLR classification tasks of ‘VLR complex image’ and ‘VLR real-world digit’. Experimental results demonstrate the superiority of the proposed model, achieving state-of-the-art (SOTA) results in both VLR complex image and VLR real-world digit domains while using fewer parameters compared to previous SOTA networks. Specifically, on the VLR CIFAR-10 dataset, the proposed model attains a 3.17% improvement in detection accuracy over the current benchmarks, and, on the VLR SVHN dataset, it achieves a 3.85% improvement by using 80% fewer parameters.\",\"PeriodicalId\":13079,\"journal\":{\"name\":\"IEEE Access\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10697178\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Access\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10697178/\",\"RegionNum\":3,\"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":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10697178/","RegionNum":3,"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}
Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule Attention
Despite extensive applications in surveillance and remote sensing, research on very low-resolution (VLR) image classification remains relatively unexplored in comparison to high-resolution (HR) image classification. We introduce a deep hybrid network that integrates capsule routing networks with a two-layer attention module. In the proposed architecture, the attention mechanism captures the more salient features, and the capsule network encodes these features to be robust to resolution changes. To enhance the network’s performance, a transfer learning on a custom image dataset, which is well-aligned to CIFAR-10, is utilized. The proposed model (Codes for the models are available at:
https://github.com/kdhasi/Deep-CapsuleAttention.git
) is evaluated on two VLR classification tasks of ‘VLR complex image’ and ‘VLR real-world digit’. Experimental results demonstrate the superiority of the proposed model, achieving state-of-the-art (SOTA) results in both VLR complex image and VLR real-world digit domains while using fewer parameters compared to previous SOTA networks. Specifically, on the VLR CIFAR-10 dataset, the proposed model attains a 3.17% improvement in detection accuracy over the current benchmarks, and, on the VLR SVHN dataset, it achieves a 3.85% improvement by using 80% fewer parameters.
IEEE AccessCOMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍:
IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest.
IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on:
Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals.
Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering.
Development of new or improved fabrication or manufacturing techniques.
Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.