Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

By Chaojian Yu, Xinyi Zhao, Qi Zheng, Peng Zhang, Xinge You. European Conference on Computer Vision, 2018

328 citations recorded by the source corpus.

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 based 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/5d608b616efb2ae57669003b6c1067d1bb7c0b4c

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Citations in the corpus

Fine-Grained Image Analysis With Deep Learning: A Survey (2021, methodology): “[103] used a dimension reduction projection before bilinear pooling to alleviate dimension explosion.”
Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (2020, unknown): “9 HBP [24] 87.”
Destruction and Construction Learning for Fine-Grained Image Recognition (2019, methodology): “This method was later extended to a series of related works with further improvements [12, 4, 8].”
X-Linear Attention Networks for Image Captioning (2020, methodology): “Inspired by the recent successes of bilinear pooling applied in fine-grained visual recognition [9, 42] or visual question answering [8, 15], we fully capitalize on bilinear pooling techniques to construct a unified attention module (X-Linear attention block) for image captioning, as depicted in Fig”
TransFG: A Transformer Architecture for Fine-grained Recognition (2021, methodology): “[50] proposed a hierarchical framework to do crosslayer bilinear pooling.”
Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up (2019, methodology): “3% respectively, outperforming all other algorithms in this comparison [53, 48, 45, 27].”