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http://hdl.handle.net/10397/82316
| 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 https://creativecommons.org/licenses/by/4.0/ 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 https://dx.doi.org/10.1109/ACCESS.2020.2978980 |
| Appears in Collections: | Journal/Magazine Article |
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|---|---|---|---|---|
| Xi_Learning_GAN_Low.pdf | 8.06 MB | Adobe PDF | View/Open |
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