Visual recognition in very low-quality settings: Delving into the power of pre-training

Bowen Cheng, Ding Liu, Zhangyang Wang, Haichao Zhang, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visual recognition from very low-quality images is an extremely challenging task with great practical values. While deep networks have been extensively applied to low-quality image restoration and high-quality image recognition tasks respectively, few works have been done on the important problem of recognition from very low-quality images. This paper presents a degradation-robust pre-training approach on improving deep learning models towards this direction. Extensive experiments on different datasets validate the effectiveness of our proposed method.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI Press
Pages8065-8066
Number of pages2
ISBN (Electronic)9781577358008
StatePublished - 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Visual recognition in very low-quality settings: Delving into the power of pre-training'. Together they form a unique fingerprint.

Cite this