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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorXi, Y-
dc.creatorZheng, JB-
dc.creatorJia, WJ-
dc.creatorHe, XJ-
dc.creatorLi, HH-
dc.creatorRen, ZQ-
dc.creatorLam, K-
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see
dc.rightsThe 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
dc.subjectImage resolutionen_US
dc.subjectObject recognitionen_US
dc.subjectSignal resolutionen_US
dc.subjectFeature extractionen_US
dc.subjectImage recognitionen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectTask analysisen_US
dc.subjectConvolutional neural networksen_US
dc.subjectGenerative adversarial networksen_US
dc.subjectLow resolution object recognitionen_US
dc.subjectRepresentation learningen_US
dc.titleSee clearly in the distance : representation learning GAN for low resolution object recognitionen_US
dc.typeJournal/Magazine Articleen_US
dcterms.abstractIdentifying 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.-
dcterms.bibliographicCitationIEEE access, 2020, v. 8, p. 53203-53214-
dcterms.isPartOfIEEE access-
dc.description.validate202006 bcrc-
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