PFNet: a novel part fusion network for fine-grained visual categorization

The existing methods in fine-grained visual categorization focus on integrating multiple deep CNN models or complicated attention mechanism, resulting in increasing cumbersome networks. In addition, most methods rely on part annotations which requires expensive expert guidance. In this paper, without extra annotation, we propose a novel part fusion network (PFNet) to effectively fuse discriminative image parts for classification. More specifically, PFNet consists of a part feature extractor to extract part features and a two-level classification network to utilize part-level and image-level features simultaneously. Part-level features are trained with the weighted part loss, which embeds a weighting mechanism based on different parts’ characteristics. Easy parts, hard parts and background parts are proposed and discriminatively used for classification. Moreover, part-level features are fused to form an image-level feature so as to introduce global supervision and generate final predictions. Experiments on three popular benchmark datasets show that our framework achieves competitive performance compared with the state-of-the-art. Code is available at https://github.com/MichaelLiang12/PFNet-FGVC.