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Yang Hua
Researcher at Queen's University Belfast
Publications - 86
Citations - 1821
Yang Hua is an academic researcher from Queen's University Belfast. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 16, co-authored 69 publications receiving 1240 citations. Previous affiliations of Yang Hua include French Institute for Research in Computer Science and Automation & Panasonic.
Papers
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Proceedings ArticleDOI
Ranked List Loss for Deep Metric Learning
TL;DR: This work presents two limitations of existing ranking-motivated structured losses and proposes a novel ranked list loss to solve both of them and proposes to learn a hypersphere for each class in order to preserve the similarity structure inside it.
Proceedings ArticleDOI
Contextualizing object detection and classification
TL;DR: This paper adopts a new method for adaptive context modeling and iterative boosting that achieves the state-of-the-art performance on object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets.
Journal ArticleDOI
Contextualizing Object Detection and Classification
TL;DR: This paper adopts a new method for adaptive context modeling and iterative boosting that achieves the state-of-the-art performance on object classification and detection tasks of PASCAL Visual Object Classes Challenge (VOC) 2007, 2010 and SUN09 data sets.
Proceedings ArticleDOI
Online Object Tracking with Proposal Selection
TL;DR: This paper formulating it as a proposal selection task and making two contributions, introducing novel proposals estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location.
Proceedings ArticleDOI
Hierarchical matching with side information for image classification
TL;DR: A hierarchical matching framework with so-called side information for image classification based on bag-of-words representation and two exemplar algorithms based on two types of side information: object confidence map and visual saliency map, from object detection priors and within-image contexts respectively are designed.