Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition

Yaming Wang; Vlad I. Morariu; Larry S. Davis

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arXiv · 1611.09932

Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition

Yaming Wang·Vlad I. Morariu·Larry S. Davis

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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 provided to understand our approach.

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BibTeX RIS

Yaming Wang, Vlad I. Morariu, Larry S. Davis. 2018-06-12. Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition. https://arxiv.org/abs/1611.09932

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