Learning a Discriminative Filter Bank Within a CNN for Fine Grained Recognition
Compared to earlier multistage frameworks using CNN features, recent end to end deep approaches for fine grained recognition essentially enhance the mid level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics. We show that mid level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class specific discriminative patches without extra part or bounding box annotations. Such a filter bank is well structured, properly initialized and discriminatively learned through a novel asymmetric multi stream architecture with convolutional filter supervision and a non random layer initialization. Experimental results show that our approach achieves state of the art on three publicly available fine grained recognition datasets (CUB 200 2011, Stanford Cars and FGVC Aircraft) Ablation studies and visualizations are provided to understand our approach.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition