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Qixiang Ye
Researcher at Chinese Academy of Sciences
Publications - 254
Citations - 9877
Qixiang Ye is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 41, co-authored 221 publications receiving 6065 citations. Previous affiliations of Qixiang Ye include University of Maryland, College Park.
Papers
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Journal ArticleDOI
Text Detection and Recognition in Imagery: A Survey
Qixiang Ye,David Doermann +1 more
TL;DR: This review provides a fundamental comparison and analysis of the remaining problems in the field and summarizes the fundamental problems and enumerates factors that should be considered when addressing these problems.
Proceedings ArticleDOI
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
TL;DR: Li et al. as mentioned in this paper proposed to preserve self-similarity of an image before and after translation, and domain-dissimilarity of a translated source image and a target image.
Proceedings ArticleDOI
Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
Shaohui Lin,Rongrong Ji,Chenqian Yan,Baochang Zhang,Liujuan Cao,Qixiang Ye,Feiyue Huang,David Doermann +7 more
TL;DR: This paper proposes an effective structured pruning approach that jointly prunes filters as well as other structures in an end-to-end manner and effectively solves the optimization problem by generative adversarial learning (GAL), which learns a sparse soft mask in a label-free and an end to end manner.
Journal ArticleDOI
Fast and robust text detection in images and video frames
TL;DR: A novel coarse-to-fine algorithm that is able to locate text lines even under complex background is proposed and Experimental results show that this approach can fast and robustly detect text lines under various conditions.
Posted Content
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
TL;DR: Li et al. as mentioned in this paper proposed to preserve self-similarity of an image before and after translation, and domain-dissimilarity of a translated source image and a target image.