M-GCN: Brain-inspired memory graph convolutional network for multi-label image recognition

2022 ◽  
Author(s):  
Xiao Yao ◽  
Feiyang Xu ◽  
Min Gu ◽  
Peipei Wang
Keyword(s):  
Image Recognition ◽  
Convolutional Network

Deep Superpixel Convolutional Network for Image Recognition

2021 ◽  
pp. 1-1
Author(s):  
Xianfang Zeng ◽  
Wenxuan Wu ◽  
Guanzhong Tian ◽  
Fuxin Li ◽  
Yong Liu
Keyword(s):  
Image Recognition ◽  
Convolutional Network

Semantic-Interactive Graph Convolutional Network for Multilabel Image Recognition

2021 ◽  
pp. 1-13
Author(s):  
Bingzhi Chen ◽  
Zheng Zhang ◽  
Yao Lu ◽  
Fanglin Chen ◽  
Guangming Lu ◽  
...  
Keyword(s):  
Image Recognition ◽  
Convolutional Network

scholarly journals Two-Level Progressive Attention Convolutional Network for Fine-Grained Image Recognition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 104985-104995
Author(s):  
Hua Wei ◽  
Ming Zhu ◽  
Bo Wang ◽  
Jiarong Wang ◽  
Deyao Sun
Keyword(s):  
Image Recognition ◽  
Convolutional Network ◽  
Fine Grained

scholarly journals THERMALNET: A DEEP CONVOLUTIONAL NETWORK FOR SYNTHETIC THERMAL IMAGE GENERATION

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 41-45 ◽  
Author(s):  
V. V. Kniaz ◽  
V. S. Gorbatsevich ◽  
V. A. Mizginov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Recognition ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
Image Generation ◽  
Deep Convolutional Neural Networks ◽  
Convolutional Network ◽  
Visible Image ◽  
Image Datasets

Deep convolutional neural networks have dramatically changed the landscape of the modern computer vision. Nowadays methods based on deep neural networks show the best performance among image recognition and object detection algorithms. While polishing of network architectures received a lot of scholar attention, from the practical point of view the preparation of a large image dataset for a successful training of a neural network became one of major challenges. This challenge is particularly profound for image recognition in wavelengths lying outside the visible spectrum. For example no infrared or radar image datasets large enough for successful training of a deep neural network are available to date in public domain. Recent advances of deep neural networks prove that they are also capable to do arbitrary image transformations such as super-resolution image generation, grayscale image colorisation and imitation of style of a given artist. Thus a natural question arise: how could be deep neural networks used for augmentation of existing large image datasets? This paper is focused on the development of the Thermalnet deep convolutional neural network for augmentation of existing large visible image datasets with synthetic thermal images. The Thermalnet network architecture is inspired by colorisation deep neural networks.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

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