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Layer normalization. arXiv:160706450. 2016."},{"key":"ref9","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11976","article-title":"A convnet for the 2020s","author":"Liu","year":"2022"},{"key":"ref10","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11963","article-title":"Scaling up your kernels to 31 \u00d7 31: revisiting large kernel design in CNNs","author":"Ding","year":"2022"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"10960","DOI":"10.1109\/TPAMI.2023.3263824","article-title":"GFNet: global filter networks for visual recognition","volume":"45","author":"Rao","year":"2023","journal-title":"IEEE Transact Patt Anal Mach Intell"},{"key":"ref12","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10819","article-title":"Metaformer is actually what you need for vision","author":"Yu","year":"2022"},{"key":"ref13","unstructured":"Howard AG. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:170404861. 2017."},{"key":"ref14","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"1492","article-title":"Aggregated residual transformations for deep neural networks","author":"Xie","year":"2017"},{"key":"ref15","first-page":"10353","article-title":"Hornet: efficient high-order spatial interactions with recursive gated convolutions","volume":"35","author":"Rao","year":"2022","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref16","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16133","article-title":"Convnext v2: co-designing and scaling convnets with masked autoencoders","author":"Woo","year":"2023"},{"key":"ref17","series-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"248","article-title":"Imagenet: a large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"ref18","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"1314","article-title":"Searching for mobilenetv3","author":"Howard","year":"2019"},{"key":"ref19","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"ref20","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: a survey","volume":"8","author":"Guo","year":"2022","journal-title":"Computat Vis Media"},{"key":"ref21","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"783","article-title":"Fcanet: frequency channel attention networks","author":"Qin","year":"2021"},{"key":"ref22","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11534","article-title":"ECA-Net: efficient channel attention for deep convolutional neural networks","author":"Wang","year":"2020"},{"key":"ref23","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11794","article-title":"Gated channel transformation for visual recognition","author":"Yang","year":"2020"},{"key":"ref24","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref25","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"ref26","unstructured":"Mehta S, Rastegari M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer. arXiv:211002178. 2021."},{"key":"ref27","series-title":"European Conference on Computer Vision","first-page":"3","article-title":"Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications","author":"Maaz","year":"2022"},{"key":"ref28","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5270","article-title":"Mobile-former: bridging mobilenet and transformer","author":"Chen","year":"2022"},{"key":"ref29","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"5785","article-title":"FastViT: a fast hybrid vision transformer using structural reparameterization","author":"Vasu","year":"2023"},{"key":"ref30","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"16889","article-title":"Rethinking vision transformers for mobilenet size and speed","author":"Li","year":"2023"},{"key":"ref31","unstructured":"Li J, Xia X, Li W, Li H, Wang X, Xiao X, et al. Next-vit: next generation vision transformer for efficient deployment in realistic industrial scenarios. arXiv:220705501. 2022."},{"key":"ref32","unstructured":"Shazeer N. Fast transformer decoding: one write-head is all you need. arXiv:191102150. 2019."},{"key":"ref33","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"12021","article-title":"Run, don\u2019t walk: chasing higher FLOPS for faster neural networks","author":"Chen","year":"2023"},{"key":"ref34","series-title":"European Conference on Computer Vision","first-page":"351","article-title":"Dynamic relu","author":"Chen","year":"2020"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"911","DOI":"10.3390\/electronics12040911","article-title":"An n-sigmoid activation function to improve the squeeze-and-excitation for 2D and 3D deep networks","volume":"12","author":"Mulindwa","year":"2023","journal-title":"Electronics"},{"key":"ref36","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"702","article-title":"Randaugment: practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"ref37","series-title":"International Conference on Learning Representations","article-title":"Mixup: beyond empirical risk minimization","author":"Zhang","year":"2018"},{"key":"ref38","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"6023","article-title":"Cutmix: regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"key":"ref39","first-page":"13001","article-title":"Random erasing data augmentation","volume":"34","author":"Zhong","year":"2020","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref40","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2818","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"ref41","series-title":"Computer Vision\u2013ECCV 2016: 14th European Conference","first-page":"646","article-title":"Deep networks with stochastic depth","author":"Huang","year":"2016 Oct 11\u201314"},{"key":"ref42","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"32","article-title":"Going deeper with image transformers","author":"Touvron","year":"2021"},{"key":"ref43","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1137\/0330046","article-title":"Acceleration of stochastic approximation by averaging","volume":"30","author":"Polyak","year":"1992","journal-title":"SIAM J Cont Optimiz"},{"key":"ref44","author":"Krizhevsky","year":"2009","journal-title":"Handbook of systemic autoimmune diseases"},{"key":"ref45","series-title":"2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing","first-page":"722","article-title":"Automated flower classification over a large number of classes","author":"Nilsback","year":"2008"},{"key":"ref46","series-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops","article-title":"3D object representations for fine-grained categorization","author":"Krause","year":"2013"},{"key":"ref47","unstructured":"Maji S, Kannala J, Rahtu E, Blaschko M, Vedaldi A. Fine-grained visual classification of aircraft. arXiv:1306.5151. 2013."},{"key":"ref48","series-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision","first-page":"983","article-title":"Ablation-cam: visual explanations for deep convolutional network via gradient-free localization","author":"Desai","year":"2020"},{"key":"ref49","series-title":"Computer Vision\u2013ECCV 2014: 13th European Conference","first-page":"740","article-title":"Microsoft coco: common objects in context","author":"Lin","year":"2014 Sep 6\u201312"},{"key":"ref50","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"2961","article-title":"Mask R-CNN","author":"He","year":"2017"},{"key":"ref51","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"6154","article-title":"Cascade R-CNN: delving into high quality object detection","author":"Cai","year":"2018"},{"key":"ref52","series-title":"International Conference on Learning Representations","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2018"},{"key":"ref53","unstructured":"Wightman R. 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