1. Multiscale attention dynamic aware network for fine‐grained visual categorization
- Author
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Jichu Ou, Wanyi Li, Jingmin Huang, Xiaojie Huang, and Xuan Xie
- Subjects
data mining ,image classification ,image recognition ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Abstract Fine‐grained visual categorization (FGVC) is a challenging task, facing the issues such as inter‐class similarities, large intra‐class variances, scale variation, and angle variation. To address these issues, the authors propose a novel multiscale attention dynamic aware network (MADA‐Net). The core of network consists of three parallel sub‐networks, which learn features from different scales. Each sub‐network is composed of three serial sub‐modules: (1) A self‐attention module (SAM) locates objects according to relative importance scattered throughout feature map. (2) A multiscale feature extractor (MFE) learns the non‐linear features of objects. (3) A dynamic aware module (DAM) enhances the learning capability of spatial deformation of the network to generate high‐quality feature map. In addition, the authors propose a multiscale adjusted loss (MA‐Loss) to improve the performance of network. Experiments on three prevailing benchmark datasets demonstrate that our method can achieve state‐of‐the‐art performance.
- Published
- 2023
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