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Advances in fine-grained visual categorization

Abstract:

The objective of this work is to improve performance in fine-grained visual categorization (FGVC). In particular, we are interested in the large-scale classification between hundreds of different flower, bird, dog species. FGVC is challenging due to high intra-class variances caused by deformation, view angle, illumination and occlusion, and low inter-class variance since some categories only differ in detail that only experts notice. Applications include field guides, automatic image anno...

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
New College
Role:
Author

Contributors

Division:
MPLS
Department:
Engineering Science
Role:
Supervisor
Division:
MPLS
Department:
Engineering Science
Role:
Supervisor
Publication date:
2015
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
Language:
English
Keywords:
Subjects:
UUID:
uuid:f5dc5e73-118b-470c-900b-b7fce1d85786
Local pid:
ora:11613
Deposit date:
2015-06-09

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