Deep learning for visual understanding: A review

By Yanming Guo, Yu Liu, Ard A. J. Oerlemans, Songyang Lao, Song Wu, Michael S. Lew. 2016

2,298 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Continual Lifelong Learning with Neural Networks: A Review (2018, background): “Modulatory feedback in Hebbian neural networks has received increasing attention, with different approaches proposing biologically plausible learning through modulatory loops (Grant, Tanner, & Itti, 2017; Soltoggio et al., 2017). For a critical review of the temporal aspects of Hebbian and homeostat”
A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “We refer readers to Deng (2014) and Guo et al. (2016) for a comprehensive overview of deep learning techniques for perception.”
Lifelong learning of human actions with deep neural network self-organization (2017, methodology): “…obtain invariant responses by alternating layers of feature detectors and nonlinear pooling neurons using the maximum (MAX) operation, which has been shown to achieve higher feature specificity and more robust invariance with respect to linear summation (see Guo et al., 2016 for a review).”
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (2022, background): “However, they rely on shuffled, balanced, fixed datasets and stationary environments (LeCun et al., 2015; Silver et al., 2016; Guo et al., 2016).”
Deep Learning for Health Informatics (2017, background): “The main disadvantage of a DBM is the time complexity required for the inference that is considerably higher with respect to the DBN, and that makes the optimization of the parameters not practical for big training set [41].”
Recent Advances in Convolutional Neural Network Acceleration (2018, background): “pers summarize the relevant work regarding deep neural networks in different applications such as computer vision, natural language processing and acoustic signal processing, among which CNN is only a component [13, 14, 15, 16, 17, 18].”
Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “The training process is quite similar to that of the traditional neural network with backpropagation (Guo et al. 2016).”
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “Deep Learning for Computer Vision: A Brief Review [229] A Survey on Deep Learning Methods for Robot Vision [230] Deep learning for visual understanding: A review [228] Deep Learning Advances in Computer Vision with 3D Data: A Survey [231] Visualizations of Deep Neural Networks in Computer Vision: A ”
Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey (2017, methodology): “Deep learning-based methods mainly include convolutional neural networks (CNN), restricted Boltzmann machines (RBMs), autoencoder, and sparse coding [9].”