Efficient multi-granularity network for fine-grained image classification

Fine-grained visual classification (FGVC) is widely used to identify different sub-categories of ships, dogs, flowers, and so on, and aims to help the ordinary people distinguish sub-categories with only slight differences. It mainly faces the challenges of small inter-class differences and large intra-class variations. The current effective methods adopt multi-scale or multi-granularity feature to find the subtle difference. However, these methods pay their attentions to the accuracy while neglecting the computational cost in practice. Therefore, in this paper, an improved efficient Multi-granularity Learning method with Only Forward Once (MLOFO) is proposed. It reduces the forward and back propagation in training from several times to once, and decreases the computational cost several times. And more, an intra-class metric loss, named prototype metric (PM) loss, is proposed to supervise learning the effective features for classification in a multi-granularity network (MGN) framework. The effectiveness of the proposed method is verified on four fine-grained classification datasets (CUB-200-2011, Stanford Cars, FGVC-Aircraft, and AircraftCarrier). Experimental results demonstrate that our method achieves state-of-the-art accuracies, substantially improving FGVC tasks. Furthermore, we discuss that the new PM loss can compress the distribution of the intra-class features as label smoothing to achieve better generalization ability. Our method is helpful to promote the training efficiency of the MGN model and improve the accuracy of fine-grained classification to a certain extent.