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Title: See clearly in the distance : representation learning GAN for low resolution object recognition
Authors: Xi, Y
Zheng, JB
Jia, WJ
He, XJ
Li, HH
Ren, ZQ
Lam, K 
Issue Date: 2020
Source: IEEE access, 2020, v. 8, p. 53203-53214
Abstract: Identifying tiny objects with extremely low resolution is generally considered a very challenging task even for human vision, due to limited information presented inside the object areas. There have been very limited attempts in recent years to deal with low-resolution recognition. The existing solutions rely on either generating super-resolution images or learning multi-scale features. However, their performance improvement becomes very limited, especially when the resolution becomes very low. In this paper, we propose a Representation Learning Generative Adversarial Network (<italic>RL</italic>-GAN) to generate super <italic>image representation</italic> that is optimized for recognition. Our solution deals with the classical vision task of object recognition in the distance. We evaluate our idea on the challenging task of low-resolution object recognition. Comparison of experimental results conducted on public and our newly created WIDER-SHIP datasets demonstrate the effectiveness of our <italic>RL</italic>-GAN, which improves the classification results significantly, with 10 & x2013;15 & x0025; gain on average, compared with benchmark solutions.
Keywords: Image resolution
Object recognition
Signal resolution
Feature extraction
Image recognition
Generative adversarial networks
Task analysis
Convolutional neural networks
Generative adversarial networks
Low resolution object recognition
Representation learning
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE access 
EISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2978980
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
The following publication Y. Xi et al., "See Clearly in the Distance: Representation Learning GAN for Low Resolution Object Recognition," in IEEE Access, vol. 8, pp. 53203-53214, 2020 is available at
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