The proposed framework is inspired by human behaviors: (1) humans always use what they learn from one task to help learn another related task; (2) humans instinctively compare images of the same/different categories and try to find the commonalities/differences between them. The proposed framework is designed to simulate these human behaviors. Extensive experimental results show that the proposed framework can notably improve the recognition accuracy of fine-grained image recognition with various backbone network architectures and on various public datasets.
The scripts need the following dependencies pre-installed:
Save the folders as:
CRA-CNN
├── CUB_Bird
├── layers
├── autonn-master
├── matconvnet-1.0-beta25
├── mcnExtraLayers-master
├── mcnPyTorch-master
Unzip the files bird-res50-biAtt.zip and bird-res50-biAtt-PRE.zip, and you can obtain bird-res50-biAtt.mat and bird-res50-biAtt-PRE.mat. bird-res50-biAtt.mat is the CRA-CNN for training on the CUB-200-2011 dataset, and the initial weights of its backbone ResNet50 are pre-trained on ImageNet. bird-res50-biAtt-PRE.mat is also the CRA-CNN for training on the CUB-200-2011 dataset, and the initial weights of its backbone ResNet50 are pre-trained on CUB-200-2011 (ART module, fully-connected classifiers, etc., are randomly initialized). You can choose to load bird-res50-biAtt.mat or bird-res50-biAtt-PRE.mat to run train_network.m. Or you can also download backbones from MCN-Models and construct CRA-CNN by yourself by referring to bird-res50-biAtt.mat or bird-res50-biAtt-PRE.mat.
To reproduce the best accuracy reported in the BMVC paper at the inference phase, please only resize and do not crop the input images. Then for each input image, you will obtain a tensor whose size is h×w×num_of_classes (h and w vary between different specific images) at the last fully-connected layer (i.e., the classifier). The final prediction score for the input image is computed by averaging the h×w×num_of_classes tensor into 1×1×num_of_classes prediction score.