See Clearly in the Distance: Representation Learning GAN for Low Resolution Object Recognition

Yue Xi, Jiangbin Zheng, Wenjing Jia, Xiangjian He, Hanhui Li, Zhuqiang Ren, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


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 (RL-GAN) to generate super image representation 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 RL-GAN, which improves the classification results significantly, with 10-15% gain on average, compared with benchmark solutions.

Original languageEnglish
Article number9026982
Pages (from-to)53203-53214
Number of pages12
JournalIEEE Access
Publication statusPublished - Mar 2020


  • Convolutional neural networks
  • generative adversarial networks
  • low resolution object recognition
  • representation learning

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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