{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:28:26Z","timestamp":1780356506636,"version":"3.54.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772252"],"award-info":[{"award-number":["61772252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Scientific Research Foundation of the Education Department of Liaoning Province","award":["LJKZ0965"],"award-info":[{"award-number":["LJKZ0965"]}]},{"name":"Huzhou Science and Technology Plan Project","award":["2022GZ08 and 2023ZD2004"],"award-info":[{"award-number":["2022GZ08 and 2023ZD2004"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"DOI":"10.1007\/s11063-024-11680-3","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T04:02:07Z","timestamp":1723521727000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Image Classification Based on Low-Level Feature Enhancement and Attention Mechanism"],"prefix":"10.1007","volume":"56","author":[{"given":"Yong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueqin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"11680_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108647","volume":"107","author":"D Pathak","year":"2023","unstructured":"Pathak D, Raju USN (2023) Shuffled-Xception-DarkNet-53: a content-based image retrieval model based on deep learning algorithm. Comput Electr Eng 107:108647","journal-title":"Comput Electr Eng"},{"issue":"6","key":"11680_CR2","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.1109\/TIP.2010.2044957","volume":"19","author":"Z Guo","year":"2010","unstructured":"Guo Z, Zhang L, Zhang D (2010) A completed modeling of local binary pattern operator for texture classification. IEEE Trans Image Process 19(6):1657\u20131663","journal-title":"IEEE Trans Image Process"},{"key":"11680_CR3","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886\u2013893. IEEE","DOI":"10.1109\/CVPR.2005.177"},{"key":"11680_CR4","doi-asserted-by":"crossref","unstructured":"Lowe DG (1999) Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, pp. 1150\u20131157. IEEE","DOI":"10.1109\/ICCV.1999.790410"},{"key":"11680_CR5","first-page":"24261","volume":"34","author":"IO Tolstikhin","year":"2021","unstructured":"Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, Yung J, Keysers D, Uszkoreit J, Lucic M, Dosovitskiy A (2021) MLP-Mixer: an all-MLP architecture for vision. Adv Neural Inf Process Syst 34:24261\u201324272","journal-title":"Adv Neural Inf Process Syst"},{"key":"11680_CR6","unstructured":"Liu B, Zhu Y, Song K, Elgammal A (2021) Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis. arXiv preprint ArXiv:2101.04775"},{"key":"11680_CR7","doi-asserted-by":"publisher","first-page":"9095","DOI":"10.1109\/ACCESS.2023.3239671","volume":"11","author":"V Patel","year":"2023","unstructured":"Patel V, Chaurasia V, Mahadeva R, Patole SP (2023) GARL-Net: graph based adaptive regularized learning deep network for breast cancer classification. IEEE Access 11:9095\u20139112","journal-title":"IEEE Access"},{"issue":"14","key":"11680_CR8","doi-asserted-by":"publisher","first-page":"6422","DOI":"10.3390\/app11146422","volume":"11","author":"H Ayaz","year":"2021","unstructured":"Ayaz H, Rodr\u00edguez-Esparza E, Ahmad M, Oliva D, P\u00e9rez-Cisneros M, Sarkar R (2021) Classification of apple disease based on non-linear deep features. Appl Sci 11(14):6422","journal-title":"Appl Sci"},{"issue":"2","key":"11680_CR9","doi-asserted-by":"publisher","first-page":"428","DOI":"10.3390\/rs15020428","volume":"15","author":"D Liu","year":"2023","unstructured":"Liu D, Wang Y, Liu P, Li Q, Yang H, Chen D, Liu Z, Han G (2023) A multi-scale cross interaction attention network for hyperspectral image classification. Remote Sens 15(2):428","journal-title":"Remote Sens"},{"issue":"1","key":"11680_CR10","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s10878-022-00952-0","volume":"45","author":"P Prasenan","year":"2023","unstructured":"Prasenan P, Suriyakala C (2023) Novel modified convolutional neural network and FFA algorithm for fish species classification. J Comb Optim 45(1):16","journal-title":"J Comb Optim"},{"issue":"1","key":"11680_CR11","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s11063-021-10555-1","volume":"55","author":"KD Gupta","year":"2023","unstructured":"Gupta KD, Sharma DK, Ahmed S, Gupta H, Gupta D, Hsu C-H (2023) A novel lightweight deep learning-based histopathological image classification model for IoMT. Neural Process Lett 55(1):205\u2013228","journal-title":"Neural Process Lett"},{"key":"11680_CR12","volume":"115","author":"J Jin","year":"2022","unstructured":"Jin J, Zhou W, Ye L, Lei J, Yu L, Qian X, Luo T (2022) DASFNet: dense-attention\u2013similarity-fusion network for scene classification of dual-modal remote-sensing images. Int J Appl Earth Obs Geoinf 115:103087","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"22","key":"11680_CR13","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6851","volume":"34","author":"D Pathak","year":"2022","unstructured":"Pathak D, Raju USN (2022) Content-based image retrieval for super-resolutioned images using feature fusion: deep learning and hand crafted. Concurr Comput: Pract Exp 34(22):e6851","journal-title":"Concurr Comput: Pract Exp"},{"key":"11680_CR14","doi-asserted-by":"crossref","unstructured":"Metwaly K, Kim A, Branson E, Monga V (2022) GlideNet: Global, local and intrinsic based dense embedding network for multi-category attributes prediction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4835\u20134846. IEEE","DOI":"10.1109\/CVPR52688.2022.00479"},{"key":"11680_CR15","doi-asserted-by":"crossref","unstructured":"Zhu L, Ji D, Zhu S, Gan W, Wu W, Yan J (2021) Learning statistical texture for semantic segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12532\u201312541. IEEE","DOI":"10.1109\/CVPR46437.2021.01235"},{"key":"11680_CR16","first-page":"6105","volume":"97","author":"M Tan","year":"2019","unstructured":"Tan M, Le Q (2019) EfficientNet: rethinking model scaling for convolutional neural networks. Proc Mach Learn Res 97:6105\u20136114","journal-title":"Proc Mach Learn Res"},{"issue":"11","key":"11680_CR17","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"11680_CR18","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556v6"},{"key":"11680_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139. IEEE","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11680_CR20","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778. IEEE","DOI":"10.1109\/CVPR.2016.90"},{"key":"11680_CR21","unstructured":"Howard AG, Zhu M, Chen B, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"11680_CR22","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: Proceedings of International Conference on Learning Representations"},{"key":"11680_CR23","first-page":"3965","volume":"34","author":"Z Dai","year":"2021","unstructured":"Dai Z, Liu H, Le Q, Tan M (2021) CoAtNet: marrying convolution and attention for all data sizes. Adv Neural Inf Process Syst 34:3965\u20133977","journal-title":"Adv Neural Inf Process Syst"},{"key":"11680_CR24","unstructured":"Yu J, Wang Z, Vasudevan V, Yeung L, Seyedhosseini M, Wu Y (2022) CoCa: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917"},{"key":"11680_CR25","first-page":"2204","volume":"27","author":"V Mnih","year":"2014","unstructured":"Mnih V, Heess N, Graves A (2014) Recurrent models of visual attention. Adv Neural Inf Process Syst 27:2204\u20132212","journal-title":"Adv Neural Inf Process Syst"},{"key":"11680_CR26","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141. IEEE","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11680_CR27","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J, Kweon IS (2018) CBAM: Convolutional block attention module. In: European Conference on Computer Vision, pp. 3\u201319. Springer","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"11680_CR28","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: Efficient channel attention for deep convolutional neural networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11531\u201311539. IEEE","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"11680_CR29","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3141\u20133149. IEEE","DOI":"10.1109\/CVPR.2019.00326"},{"key":"11680_CR30","doi-asserted-by":"crossref","unstructured":"Snoek CGM, Worring M, Smeulders AWM (2005) Early versus late fusion in semantic video analysis. In: 13th Annual ACM International Conference on Multimedia, pp. 399\u2013402. ACM","DOI":"10.1145\/1101149.1101236"},{"key":"11680_CR31","doi-asserted-by":"crossref","unstructured":"Bell S, Zitnick CL, Bala K, Girshick R (2016) Inside-Outside Net: Detecting objects in context with skip pooling and recurrent neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2874\u20132883. IEEE","DOI":"10.1109\/CVPR.2016.314"},{"key":"11680_CR32","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 936\u2013944. IEEE","DOI":"10.1109\/CVPR.2017.106"},{"key":"11680_CR33","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: Single shot multibox detector. In: European Conference on Computer Vision, pp. 21\u201337. Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"11680_CR34","doi-asserted-by":"crossref","unstructured":"Cai Z, Fan Q, Feris RS, Vasconcelos N (2016) A unified multi-scale deep convolutional neural network for fast object detection. In: European Conference on Computer Vision, pp. 354\u2013370. Springer","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"11680_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.106874","volume":"196","author":"J Zhan","year":"2022","unstructured":"Zhan J, Hu Y, Zhou G, Wang Y, Cai W, Li L (2022) A high-precision forest fire smoke detection approach based on ARGNet. Comput Electron Agric 196:106874","journal-title":"Comput Electron Agric"},{"key":"11680_CR36","doi-asserted-by":"publisher","first-page":"5520521","DOI":"10.1109\/TGRS.2023.3306891","volume":"61","author":"J Zhan","year":"2023","unstructured":"Zhan J, Xie Y, Guo J, Hu Y, Zhou G, Cai W, Wang Y, Chen A, Xie L, Li M, Li L (2023) DGPF-RENet: a low data dependence network with low training iterations for hyperspectral image classification. IEEE Trans Geosci Remote Sens 61:5520521","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"11680_CR37","doi-asserted-by":"crossref","unstructured":"Bossard L, Guillaumin M, Gool LV (2014) Food-101\u2013mining discriminative components with random forests. In: European Conference on Computer Vision, pp. 446\u2013461. Springer","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"11680_CR38","doi-asserted-by":"crossref","unstructured":"Chen J, Ngo C-W (2016) Deep-based ingredient recognition for cooking recipe retrieval. In: Proceedings of the 24th ACM International Conference on Multimedia. ACM","DOI":"10.1145\/2964284.2964315"},{"key":"11680_CR39","doi-asserted-by":"crossref","unstructured":"Kawano Y, Yanai K (2014) Automatic expansion of a food image dataset leveraging existing categories with domain adaptation. In: Proceedings of Computer Vision-ECCV 2014 Workshops, pp. 3\u201317. Springer","DOI":"10.1007\/978-3-319-16199-0_1"},{"key":"11680_CR40","doi-asserted-by":"crossref","unstructured":"Kawano Y, Yanai K (2013) Real-time mobile food recognition system. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1\u20137. IEEE","DOI":"10.1109\/CVPRW.2013.5"},{"key":"11680_CR41","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.cviu.2016.01.012","volume":"148","author":"N Martinel","year":"2016","unstructured":"Martinel N, Piciarelli C, Micheloni C (2016) A supervised extreme learning committee for food recognition. Comput Vis Image Underst 148:67\u201386","journal-title":"Comput Vis Image Underst"},{"key":"11680_CR42","doi-asserted-by":"crossref","unstructured":"Liu C, Luo Y, Chen G, Vokkarane V, Ma Y (2016) DeepFood: Deep learning-based food image recognition for computer-aided dietary assessment. In: International Conference on Smart Homes and Health Telematics, pp. 37\u201348. Springer","DOI":"10.1007\/978-3-319-39601-9_4"},{"issue":"12","key":"11680_CR43","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1109\/LSP.2017.2758862","volume":"24","author":"P Pandey","year":"2017","unstructured":"Pandey P, Deepthi A, Mandal B, Puhan NB (2017) FoodNet: recognizing foods using ensemble of deep networks. IEEE Signal Process Lett 24(12):1758\u20131762","journal-title":"IEEE Signal Process Lett"},{"key":"11680_CR44","doi-asserted-by":"crossref","unstructured":"Bolanos M, Radeva P (2016) Simultaneous food localization and recognition. In: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). IEEE","DOI":"10.1109\/ICPR.2016.7900117"},{"key":"11680_CR45","doi-asserted-by":"crossref","unstructured":"Phiphiphatphaisit S, Surinta O (2020) Food image classification with improved MobileNet architecture and data augmentation. In: International Conference on Information Science and System, pp. 51\u201356. ACM","DOI":"10.1145\/3388176.3388179"},{"key":"11680_CR46","volume-title":"Deep learning based food recognition. Technical report","author":"Q Yu","year":"2016","unstructured":"Yu Q, Mao D, Wang J (2016) Deep learning based food recognition. Technical report. Stanford University, Stanford"},{"key":"11680_CR47","doi-asserted-by":"publisher","first-page":"126832","DOI":"10.1109\/ACCESS.2022.3226517","volume":"10","author":"V Sevetlidis","year":"2022","unstructured":"Sevetlidis V, Pavlidis G, Mouroutsos S, Gasteratos A (2022) Tackling dataset bias with an automated collection of real-world samples. IEEE Access 10:126832\u2013126844","journal-title":"IEEE Access"},{"key":"11680_CR48","unstructured":"Wang F, Kong T, Zhang R, Liu H, Li H (2021) Self-supervised learning by estimating twin class distributions. arXiv preprint arXiv:2110.07402"},{"key":"11680_CR49","doi-asserted-by":"crossref","unstructured":"Sandru A, Georgescu M-I, Ionescu RT (2022) Feature-level augmentation to improve robustness of deep neural networks to affine transformations. arXiv preprint arXiv:2202.05152","DOI":"10.1007\/978-3-031-25056-9_22"},{"key":"11680_CR50","doi-asserted-by":"crossref","unstructured":"Touijer L, Pastore VP, Odone F (2023) Food image classification: The benefit of in-domain transfer learning. In: International Conference on Image Analysis and Processing, pp. 259\u2013269","DOI":"10.1007\/978-3-031-43153-1_22"},{"key":"11680_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2023.111833","volume":"365","author":"X Gao","year":"2024","unstructured":"Gao X, Xiao Z, Deng Z (2024) High accuracy food image classification via vision transformer with data augmentation and feature augmentation. J Food Eng 365:111833","journal-title":"J Food Eng"},{"key":"11680_CR52","doi-asserted-by":"crossref","unstructured":"Ege T, Yanai K (2017) Simultaneous estimation of food categories and calories with multi-task CNN. In: Proceedings of fifteenth IAPR International Conference on Machine Vision Applications (MVA), pp. 198\u2013201. IEEE","DOI":"10.23919\/MVA.2017.7986835"},{"key":"11680_CR53","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261\u20132269. IEEE","DOI":"10.1109\/CVPR.2017.243"},{"key":"11680_CR54","doi-asserted-by":"crossref","unstructured":"Chen Y, Bai Y, Zhang W, Mei T (2019) Destruction and construction learning for fine-grained image recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5152\u20135161. IEEE","DOI":"10.1109\/CVPR.2019.00530"},{"key":"11680_CR55","doi-asserted-by":"crossref","unstructured":"Klasson M, Zhang C, Kjellstr\u00f6m H (2019) A hierarchical grocery store image dataset with visual and semantic labels. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 491\u2013500. IEEE","DOI":"10.1109\/WACV.2019.00058"},{"issue":"7","key":"11680_CR56","first-page":"13001","volume":"34","author":"Z Zhong","year":"2020","unstructured":"Zhong Z, Zheng L, Kang G, Li S, Yang Y (2020) Random erasing data augmentation. Proc AAAI Conf Artif Intell 34(7):13001\u201313008","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"11680_CR57","doi-asserted-by":"crossref","unstructured":"Xu Y, Li Y, Li J, Lu C (2022) Constructing balance from imbalance for long-tailed image recognition. In: Proceedings of European Conference on Computer Vision, pp. 38\u201356. Springer","DOI":"10.1007\/978-3-031-20044-1_3"},{"key":"11680_CR58","doi-asserted-by":"crossref","unstructured":"Pan X, Ge C, Lu R, Song S, Chen G, Huang Z, Huang G (2022) On the integration of self-attention and convolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 805\u2013815. IEEE","DOI":"10.1109\/CVPR52688.2022.00089"},{"key":"11680_CR59","doi-asserted-by":"crossref","unstructured":"Chen Z, Qi Z, Li X et al (2023) Class-aware convolution and attentive aggregation for image classification. In: Proceedings of the 5th ACM International Conference on Multimedia in Asia, pp. 1\u20137. ACM","DOI":"10.1145\/3595916.3626390"},{"key":"11680_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105645","volume":"146","author":"E Aguilar","year":"2022","unstructured":"Aguilar E, Nagarajan B, Radeva P (2022) Uncertainty-aware selecting for an ensemble of deep food recognition models. Comput Biol Med 146:105645","journal-title":"Comput Biol Med"},{"key":"11680_CR61","doi-asserted-by":"crossref","unstructured":"Yanai K, Kawano Y (2015) Food image recognition using deep convolutional network with pre-training and fine-tuning. In: Proceedings of International Conference on Multimedia & Expo Workshops (ICMEW). IEEE","DOI":"10.1109\/ICMEW.2015.7169816"},{"key":"11680_CR62","doi-asserted-by":"crossref","unstructured":"Tanno R, Okamoto K, Yanai K (2016) DeepFoodCam: A DCNN-based real-time mobile food recognition system. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, pp. 89. ACM","DOI":"10.1145\/2986035.2986044"},{"key":"11680_CR63","doi-asserted-by":"crossref","unstructured":"Hassannejad H, Matrella G, Ciampolini P, Munari ID, Mordonini M, Cagnoni S (2016) Food image recognition using very deep convolutional networks. In: Proceedings of the 2nd International Workshop on Multimedia Assisted Dietary Management, pp. 41\u201349. ACM","DOI":"10.1145\/2986035.2986042"},{"key":"11680_CR64","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.patrec.2017.12.007","volume":"105","author":"EJ Heravi","year":"2018","unstructured":"Heravi EJ, Aghdam HH, Puig D (2018) An optimized convolutional neural network with bottleneck and spatial pyramid pooling layers for classification of foods. Pattern Recogn Lett 105:50\u201358","journal-title":"Pattern Recogn Lett"},{"key":"11680_CR65","doi-asserted-by":"publisher","first-page":"82328","DOI":"10.1109\/ACCESS.2020.2991810","volume":"8","author":"GA Tahir","year":"2020","unstructured":"Tahir GA, Loo CK (2020) An open-ended continual learning for food recognition using class incremental extreme learning machines. IEEE Access 8:82328\u201382346","journal-title":"IEEE Access"},{"key":"11680_CR66","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision, pp. 618\u2013626. IEEE","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11680-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/article\/10.1007\/s11063-024-11680-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11680-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T16:12:27Z","timestamp":1725120747000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/link.springer.com\/10.1007\/s11063-024-11680-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":66,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["11680"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1007\/s11063-024-11680-3","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"2 August 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no financial or personal relationships with other people or organizations that could inappropriately influence this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"217"}}