ИЕРАРХИЧЕСКИЕ БИНАРНЫЕ CNN ДЛЯ ЛОКАЛИЗАЦИИ ДОСТОПРИМЕЧАТЕЛЬНОСТЕЙ С ОГРАНИЧЕННЫМИ РЕСУРСАМИ
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Binary Convolutional Neural Networks, Residual learning, Landmark localization, Human pose estimation, Face alignment.Abstrak
Аннотация. Наша цель — разработать архитектуры, которые сохранят новаторскую производительность сверточных нейронных сетей (CNN) для ориентировочной локализации и в то же время будут легкими, компактными и подходящими для приложений с ограниченными вычислительными ресурсами.
Iqtiboslar
Begmatov Sh.A., Arabboyev M.M, Xudayberganov J.D. “ Google media-pipe kutubxonasidan foydalangan holda inson gavdasi harakatlari farqlarini baholash” Muhammad al-Xorazmiy avlodlari 3(21)2022.
A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in ECCV, 2016.
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, “Convolutional pose machines,” in CVPR, 2016.
A. Bulat and G. Tzimiropoulos, “Two-stage convolutional part heatmap regression for the 1st 3d face alignment in the wild (3dfaw) challenge,” in ECCV. Springer International Publishing, 2016, pp. 616–624.
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnornet: Imagenet classification using binary convolutional neural networks,” in ECCV, 2016.
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,” arXiv, 2016.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in ECCV, 2016.
A. Bulat and G. Tzimiropoulos, “Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources,” in ICCV, 2017.
J. L. Holi and J.-N. Hwang, “Finite precision error analysis of neural network hardware implementations,” IEEE Transactions on Computers, vol. 42, no. 3, pp. 281–290, 1993.
M. Courbariaux, Y. Bengio, and J.-P. David, “Training deep neural networks with low precision multiplications,” arXiv, 2014.
D. D. Lin, S. S. Talathi, and V. S. Annapureddy, “Fixed point quantization of deep convolutional networks,” arXiv, 2015.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009.
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou, “Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients,” arXiv, 2016.
D. Soudry, I. Hubara, and R. Meir, “Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights,” in NIPS, 2014.
M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in NIPS, 2015.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR, 2016.
S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He, “Aggregated ´ residual transformations for deep neural networks,” arXiv, 2016.
