Multiscale attention dynamic aware network for fine‐grained visual categorization

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.