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An Efficient Fine-grained Vehicle Recognition Method Based on Part-level Feature Optimization by Lei Lu; Yancheng Cai; Hua Huang; Ping Wang is a Computer Science article available to read on EtoBox.

What is An Efficient Fine-grained Vehicle Recognition Method Based on Part-level Feature Optimization about?

This paper presents an effective method for strengthening the discriminative ability of high-level deep features by enhancing and aggregating discriminative part-level features for the fine-grained vehicle recognition task. In general, the task of visual recognition concentrates more on the visual differences at the object level. However, for fine-grained object recognition, the visual differences between target objects typically exist in local discriminative areas, so it is more concerned about extracting fine-grained features from these part regions. In this context, we propose solving this issue with a novel feature extraction method from two perspectives: the generation of more feature descriptors of part regions through the learning process of deep networks and the aggregation of part-level discriminative features. This approach is designed to improve the backbone networks to generate finer-level part features through a part-level feature enhancement module and to investigate the intrinsic part-level features of the backbone networks with the help of a feature aggregation module. The enhancement module efficiently finds the finer features highly correlated to the part regions.

Who reads An Efficient Fine-grained Vehicle Recognition Method Based on Part-level Feature Optimization?

Author

Lei Lu; Yancheng Cai; Hua Huang; Ping Wang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)