Three-branch and Mutil-scale learning for Fine-grained Image Recognition (TBMSL-Net)

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is one of the most authoritative academic competitions in the field of Computer Vision (CV) in recent years, but it can not achieve good result to directly migrate the champions of the annual competition, to fine-grained visual categorization (FGVC) tasks. The small interclass variations and the large intraclass variations caused by the fine-grained nature makes it a challenging problem. The proposed method can be effectively localize object and useful part regions without the need of bounding-box and part annotations by attention object location module (AOLM) and attention part proposal module (APPM). The obtained object images contain both the whole structure and more details, the part images have many different scales and have more fine-grained features, and the raw images contain the complete object. The three kinds of training images are supervised by our three-branch network structure. The model has good classification ability, good generalization and robustness for different scale object images. Our approach is end-to-end training, through the comprehensive experiments demonstrate that our approach achieves state-of-the-art results on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets.