Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

Yaming Wang, Vlad I. Morariu, Larry S. Davis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4148-4157

Abstract


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 further provided to understand our approach.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Wang_2018_CVPR,
author = {Wang, Yaming and Morariu, Vlad I. and Davis, Larry S.},
title = {Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}