Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
TLDR
A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.About:
This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.read more
Citations
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ViCTer: A semi-supervised video character tracker
TL;DR: In this paper , a semi-supervised face recognition network and a multi-human tracker is proposed to solve the video character tracking problem, which can be applied in various video analysis-related areas, such as movie analysis and automatic video clipping.
Journal ArticleDOI
Multi-Layered Graph Convolutional Network-Based Industrial Fault Diagnosis with Multiple Relation Characterization Capability
TL;DR: An advantageous intelligent diagnosis scheme termed AE-MSGCN is proposed, which employs graph convolutional networks (GCNs) on multi-layer networks in an innovative manner and is more effective and practical than the existing state-of-the-art methods.
Journal ArticleDOI
LAD-RCNN: A Powerful Tool for Livestock Face Detection and Normalization
TL;DR: Li et al. as discussed by the authors proposed a lightweight angle detection and region-based convolutional network (LAD-RCNN) to detect livestock faces and their rotation angles with arbitrary directions in one stage.
Posted ContentDOI
Design and implementation of the intelligent system for automatically monitoring waterbirds in Quanzhou Bar Wetland
TL;DR: In this paper , an intelligent waterbird automatic identification system based on Model-View-Viewmodel (MVVM) framework was designed to support high effectively, safe and long-time monitoring the native wetland waterbirds.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
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Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.