Two-Level Progressive Attention Convolutional Network for Fine-Grained Image Recognition

The learning of discriminative features is the key for fine-grained image recognition. To better extract effective features and improve the accuracy of fine-grained image recognition, we propose a two-level progressive attention convolutional network (TPA-CNN) for fine-grained image recognition. The model includes a multi-channel attention-fusion (MCAF) module and a cross-layer element-attention (CEA) module. The MCAF module is used to find distinctive feature map channels which significantly responds to specific regions. Inspired by Hierarchical Bilinear Pooling model, The CEA module is further assign weight values to feature map elements. From the perspective of attention visualization map, MCAF module can focused on one or more positive regions, CEA module further locates the most discriminative regions by interaction between the feature map elements. The model can dynamically search the discriminative region of the image, not limited to the boost or crop a selected region. Compared to previous models basing on attention mechanism, the model can extract non-correlated part features which spread over object foreground areas, further improving the recognition accuracy. Experimental results on CUB-200-2011, FGVC-Aircraft, and Stanford Cars datasets demonstrate that the proposed TPA-CNN achieves competitive performance.