DeepBP: A bilinear model integrating multi-order statistics for fine-grained recognition

The bilinear model using second-order statistical features is an important weakly supervised method for fine-grained recognition. Based on this, fusing higher-order statistical features to obtain more discriminant features is an effective approach for improving the performance of the model. However, the existing framework is difficult to fuse higher-order features due to a sharp increase in the number of parameters caused by the increase in fusion order. To address the issue, this paper proposes a DeepBP model composed of a deep network module and a bilinear pooling module. The bilinear module explicitly captures low-order statistical features, and the deep network module implicitly learns high-order features. The two modules are integrated to achieve multi-level information integration. To verify the model's ability, experiments are conducted on the CUB, Cars, and Aircrafts datasets, and the accuracy of 85.6%, 91.6%, and 88.6% is achieved, respectively.

Automated summary

This paper proposes a DeepBP model that combines a deep network module and a bilinear pooling module to fuse low-order and high-order statistical features for multi-level information integration.