Jiabao Wang
ABSTRACT
Fine grained vehicle recognition is a sub-category classification task, specifically for vehicles. It is faced with the problems of small inter-class differences and large intra-class variants, which is very challenging. In order to obtain better recognition accuracy, based on the network architecture searched by neural architecture search (NAS), this paper proposes a novel auxiliary learning method to help learn a more robust model for fine-grained vehicle recognition. Experimental results verify the effectiveness of the proposed method on the public Stanford-Cars and self-built FGVC_LAV datasets.
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Index Terms
- NAS-based Auxiliary Learning for Fine-grained Vehicle Recognition
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