Sequence-To-Sequence Domain Adaptation Network for Robust Text Image Recognition

2019 ◽  
Author(s):  
Yaping Zhang ◽  
Shuai Nie ◽  
Wenju Liu ◽  
Xing Xu ◽  
Dongxiang Zhang ◽  
...  
Keyword(s):  
Image Recognition ◽  
Domain Adaptation ◽  
Sequence Domain

Robust Text Image Recognition via Adversarial Sequence-to-Sequence Domain Adaptation

2021 ◽  
Vol 30 ◽  
pp. 3922-3933
Author(s):  
Yaping Zhang ◽  
Shuai Nie ◽  
Shan Liang ◽  
Wenju Liu
Keyword(s):  
Image Recognition ◽  
Domain Adaptation ◽  
Sequence Domain

Domain Adaptation for Agricultural Image Recognition and Segmentation Using Category Maps

2021 ◽  
Author(s):  
Kota Takahashi ◽  
Hirokazu Madokoro ◽  
Satoshi Yamamoto ◽  
Yo Nishimura ◽  
Stephanie Nix ◽  
...  
Keyword(s):  
Image Recognition ◽  
Domain Adaptation

scholarly journals Wheel Hub Defects Image Recognition Base on Zero-Shot Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1529
Author(s):  
Xiaohong Sun ◽  
Jinan Gu ◽  
Meimei Wang ◽  
Yanhua Meng ◽  
Huichao Shi
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Large Scale ◽  
Domain Adaptation ◽  
Data Sets ◽  
Human Beings ◽  
Data Set ◽  
Defect Image ◽  
Product Surface ◽  
Projection Domain

In the wheel hub industry, the quality control of the product surface determines the subsequent processing, which can be realized through the hub defect image recognition based on deep learning. Although the existing methods based on deep learning have reached the level of human beings, they rely on large-scale training sets, however, these models are completely unable to cope with the situation without samples. Therefore, in this paper, a generalized zero-shot learning framework for hub defect image recognition was built. First, a reverse mapping strategy was adopted to reduce the hubness problem, then a domain adaptation measure was employed to alleviate the projection domain shift problem, and finally, a scaling calibration strategy was used to avoid the recognition preference of seen defects. The proposed model was validated using two data sets, VOC2007 and the self-built hub defect data set, and the results showed that the method performed better than the current popular methods.

PDA: Proxy-based domain adaptation for few-shot image recognition

2021 ◽  
Vol 110 ◽  
pp. 104164
Author(s):  
Ge Liu ◽  
Linglan Zhao ◽  
Xiangzhong Fang
Keyword(s):  
Image Recognition ◽  
Domain Adaptation

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.

Domain Adaptation for Visual Recognition

2015 ◽  
Author(s):  
Raghuraman Gopalan ◽  
Ruonan Li ◽  
Vishal M. Patel ◽  
Rama Chellappa
Keyword(s):  
Visual Recognition ◽  
Domain Adaptation
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