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Yuqian Zhou
Researcher at University of Illinois at Urbana–Champaign
Publications - 56
Citations - 2380
Yuqian Zhou is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Computer science & Image restoration. The author has an hindex of 19, co-authored 45 publications receiving 1178 citations. Previous affiliations of Yuqian Zhou include York University & Urbana University.
Papers
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Proceedings ArticleDOI
Self-Similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-Identification
TL;DR: A Self-similarity Grouping (SSG) approach, which exploits the potential similarity of unlabeled samples to build multiple clusters from different views automatically, and introduces a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting.
Journal ArticleDOI
Horizontal Pyramid Matching for Person Re-Identification
Yang Fu,Yunchao Wei,Yuqian Zhou,Honghui Shi,Gao Huang,Xinchao Wang,Zhiqiang Yao,Thomas S. Huang +7 more
TL;DR: A simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be still identified even even some key parts are missing.
Proceedings ArticleDOI
Image Super-Resolution with Non-Local Sparse Attention
Yiqun Mei,Yuchen Fan,Yuqian Zhou +2 more
TL;DR: Non-local sparse attention (NLSA) as mentioned in this paper is designed to retain long-range modeling capability from non-local operation while enjoying robustness and high-efficiency of sparse representation, which partitions the input space into hash buckets of related features.
Proceedings ArticleDOI
Image Super-Resolution With Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
TL;DR: This paper proposes the first Cross-Scale Non-Local (CS-NL) attention module with integration into a recurrent neural network and can find more cross-scale feature correlations within a single low-resolution (LR) image.
Journal ArticleDOI
Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory.
TL;DR: This work used machine learning methods on comprehensive, longitudinal clinical data from the MIMIC-III to predict the ICU readmission of patients within 30 days of their discharge to highlight the ability of machine learning models to improve ICU decision-making accuracy and is a real-world example of precision medicine in hospitals.