Fine-Grained Classification via Hierarchical Bilinear Pooling With Aggregated Slack Mask

Extracting discriminative fine-grained features is essential for fine-grained image recognition tasks. Many researchers utilize expensive human annotations to learn discriminative part models, which may be impossible for real-world applications. Recently, bilinear pooling has been frequently adopted and has shown its effectiveness owing to its learning discriminative regions automatically. However, most bilinear pooling models still utilize the all convolutional part/region features for recognition, including those noisy or even harmful feature elements. In this paper, we devise a novel fine-grained image classification approach by the Hierarchical Bilinear Pooling with Aggregated Slack Mask (HBPASM) model. The proposed model generates a RoI-aware image feature representation for better performance. We conduct experiments on three frequently used fine-grained image classification datasets. The experimental results demonstrate that HBPASM achieves competitive performance or even match the state-of-the-art methods on CUB-200-2011, Stanford Cars, and FGVC-Aircraft, respectively.