Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, Xinge You; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 574-589


Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinear pooling approaches. Second, we propose a novel hierarchical bilinear pooling framework to integrate multiple cross-layer bilinear features to enhance their representation capability. Our formulation is intuitive, efficient and achieves state-of-the-art results on the widely used fine-grained recognition datasets.

Related Material

[pdf] [arXiv]
author = {Yu, Chaojian and Zhao, Xinyi and Zheng, Qi and Zhang, Peng and You, Xinge},
title = {Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}