Filter bank

Filter (signal processing)

Deep learning

Initialization

Image (mathematics)

Machine learning

Philosophy

Minimum bounding box

Pattern recognition (psychology)

Artificial intelligence

Political science

Programming language

Representation (politics)

Feature (linguistics)

Class (philosophy)

Computer vision

Computer science

Linguistics

Feature extraction

Feature learning

Bounding overwatch

Convolutional neural network

Politics

Discriminative model

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.

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