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SE-dual path networks combined with a navigator for fine-grained classification
Author(s): Liu Yang; Jin Zhong
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Paper Abstract

Recognizing fine-grained categories is difficult due to the challenges of discriminative region localization and fine-grained feature learning. To handle this circumstance, we propose a novel model termed SDN-Net for SE-DPN-Navigator Networks, which consists of DPN (Dual Path Networks), SE-blocks (Squeeze-and-Excitation Blocks) and a Navigator. DPN shares common features while maintaining the flexibility to explore new features. Moreover, we add SE-blocks into DPN to make up the SE-DPN which acts as a feature extractor of the proposed model, SE-blocks helps the model learn to use global information to selectively emphasize informative features and suppress less useful ones. We also use a Navigator to help the model to detect most informative regions without extra bounding box/part annotations. Our model can be trained end-to-end. With the great cooperation between these three components, we achieve state-of-the-art performance on two publicly available fine-grained recognition datasets (CUB-200-2001 and Stanford Cars). Besides, We have done ablation studies and confirmed the effectiveness of each components in the proposed model.

Paper Details

Date Published: 31 July 2019
PDF: 6 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 1119809 (31 July 2019); doi: 10.1117/12.2540751
Show Author Affiliations
Liu Yang, Nanjing Univ. of Science and Technology (China)
Jin Zhong, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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