Multiscale Progressive Complementary Fusion Network for Fine-Grained Visual Classification
Jingsheng Lei, Xinqi Yang, Shengying Yang · IEEE Access · 2022
In fine-grained visual classification(FGVC), the small inter-class variations and the large intra-class variations are always inherent attributes, so it is much more challenging than traditional classification tasks. Recent works mainly tackle this problem by employing attention mechanisms to locate the most discriminative parts. However, these methods tend to neglect other inconspicuous but distinguishable parts, and can not effectively fuse the features information of different scales and different degrees of discrimination. In this work, we propose a multi-scale progressive complementary fusion network (MPCF-Net) to tackle these problems. In particular, we propose: (i) A three-step multi-scale progressive training method, which employs the image slicer to generate puzzle images at different scales followed by multi-step progressive training. This enables the network to capture multi-granularity local feature information and gradually expand its attention to the global structural information as the training progresses for multi-granularity information fusion. (ii) A plug-and-play feature complementary enhancement module (FCEM) that explicitly enhances the features extracted by the current layer of the network, while also enabling the next layer of the network to extract potential complementary feature information to diversify the features. Our experiments were conducted on four FGVC benchmark datasets and yielded state-of-the-art or competitive results.