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dc.contributor.authorChen, Zhe
dc.date.accessioned2019-06-28
dc.date.available2019-06-28
dc.date.issued2019-01-30
dc.identifier.urihttp://hdl.handle.net/2123/20654
dc.description.abstractContexts provide beneficial information for machine-based image understanding tasks. However, existing context modelling methods still cannot fully exploit contexts, especially for object recognition and detection. In this thesis, we develop augmented context modelling neural networks to better utilize contexts for different object recognition and detection tasks. Our contributions are two-fold: 1) we introduce neural networks to better model instance-level visual relationships; 2) we introduce neural network-based algorithms to better utilize contexts from 3D information and synthesized data. In particular, to augment the modelling of instance-level visual relationships, we propose a context refinement network and an encapsulated context modelling network for object detection. In the context refinement study, we propose to improve the modeling of visual relationships by introducing overlap scores and confidence scores of different regions. In addition, in the encapsulated context modelling study, we boost the context modelling performance by exploiting the more powerful capsule-based neural networks. To augment the modeling of contexts from different sources, we propose novel neural networks to better utilize 3D information and synthesis-based contexts. For the modelling of 3D information, we mainly investigate the modelling of LiDAR data for road detection and the depth data for instance segmentation, respectively. In road detection, we develop a progressive LiDAR adaptation algorithm to improve the fusion of 3D LiDAR data and 2D image data. Regarding instance segmentation, we model depth data as context to help tackle the low-resolution annotation-based training problem. Moreover, to improve the modelling of synthesis-based contexts, we devise a shape translation-based pedestrian generation framework to help improve the pedestrian detection performance.en_AU
dc.rightsThe author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.en_AU
dc.subjectDeep Learningen_AU
dc.subjectContext Modellingen_AU
dc.subjectDeep Neural Networken_AU
dc.subjectComputer Visionen_AU
dc.subjectObject Detectionen_AU
dc.titleAugmented Context Modelling Neural Networksen_AU
dc.typeThesisen_AU
dc.type.thesisDoctor of Philosophyen_AU
usyd.facultyFaculty of Engineering, School of Computer Scienceen_AU
usyd.degreeDoctor of Philosophy Ph.D.en_AU
usyd.awardinginstThe University of Sydneyen_AU


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