{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T08:52:24Z","timestamp":1742806344284},"reference-count":37,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Un. Sys."],"published-print":{"date-parts":[[2023,7]]},"abstract":" Deep convolutional neural network (CNN) models are typically trained on high-resolution images. When we apply them directly to low-resolution infrared images, for example, the performances will not always be satisfactory. This is due to CNN layers that operate in a local neighborhood, which is already poor in information for infrared images. To overcome these weaknesses and increase information of global nature, a hybrid architecture based on CNN with self-attention mechanism is proposed. This later provides information about the global context by capturing the long-range interactions between the different parts of an image. In this paper, we have incorporated a convolutional\u2013attentional form in the top layers of two pre-trained networks VGGNet and ResNet. The convolutional\u2013attentional form is a concatenation of two paths; the original convolutional feature maps of the pre-trained network, and the output of a relative multi-head attentional block. Extensive experiments are conducted in the FLIR starter thermal dataset, where we achieve a [Formula: see text] overall accuracy in the four-class FLIR starter thermal dataset. Moreover, the proposed architectures exceed the state of the art in target recognition on two-class FLIR starter thermal dataset with a [Formula: see text] improvement in overall classification accuracy. In addition, a study on the effect of different hyper-parameters and error analysis is carried out to give some research forward directions. <\/jats:p>","DOI":"10.1142\/s2301385023500085","type":"journal-article","created":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T11:20:15Z","timestamp":1651317615000},"page":"221-230","source":"Crossref","is-referenced-by-count":6,"title":["Augmented Convolutional Neural Network Models with Relative Multi-Head Attention for Target Recognition in Infrared Images"],"prefix":"10.1142","volume":"11","author":[{"given":"Billel","family":"Nebili","sequence":"first","affiliation":[{"name":"Ecole Militaire Polytechnique, UER SAI, Algiers 16111, Algeria"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5455-6528","authenticated-orcid":false,"given":"Atmane","family":"Khellal","sequence":"additional","affiliation":[{"name":"Ecole Militaire Polytechnique, UER SAI, Algiers 16111, Algeria"}]},{"given":"Abdelkrim","family":"Nemra","sequence":"additional","affiliation":[{"name":"Ecole Militaire Polytechnique, UER SAI, Algiers 16111, Algeria"}]},{"given":"Laurent","family":"Mascarilla","sequence":"additional","affiliation":[{"name":"Laboratoire MIA, Universit\u00e9 de La Rochelle, Avenue Michel Cr\u00e9peau, F-17042 La Rochelle Cedex, France"}]}],"member":"219","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"S2301385023500085BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S2301385022500029"},{"key":"S2301385023500085BIB002","doi-asserted-by":"publisher","DOI":"10.1142\/S2301385022500030"},{"key":"S2301385023500085BIB003","doi-asserted-by":"publisher","DOI":"10.1142\/S2301385015400038"},{"key":"S2301385023500085BIB004","volume-title":"3rd Int. 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