BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification

• URL: http://arxiv.org/abs/2011.14311v1
• Date: Sun, 29 Nov 2020 08:38:17 GMT
• Title: BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
• Authors: Xiaoxu Li, Jijie Wu, Zhuo Sun, Zhanyu Ma, Jie Cao, Jing-Hao Xue
• Abstract summary: We propose a so-called textitBi-Similarity Network (textitBSNet) The bi-similarity module learns feature maps according to two similarity measures of diverse characteristics. In this way, the model is enabled to learn more discriminative and less similarity-biased features from few shots of fine-grained images.
• Score: 35.50808687239441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot learning for fine-grained image classification has gained recent attention in computer vision. Among the approaches for few-shot learning, due to the simplicity and effectiveness, metric-based methods are favorably state-of-the-art on many tasks. Most of the metric-based methods assume a single similarity measure and thus obtain a single feature space. However, if samples can simultaneously be well classified via two distinct similarity measures, the samples within a class can distribute more compactly in a smaller feature space, producing more discriminative feature maps. Motivated by this, we propose a so-called \textit{Bi-Similarity Network} (\textit{BSNet}) that consists of a single embedding module and a bi-similarity module of two similarity measures. After the support images and the query images pass through the convolution-based embedding module, the bi-similarity module learns feature maps according to two similarity measures of diverse characteristics. In this way, the model is enabled to learn more discriminative and less similarity-biased features from few shots of fine-grained images, such that the model generalization ability can be significantly improved. Through extensive experiments by slightly modifying established metric/similarity based networks, we show that the proposed approach produces a substantial improvement on several fine-grained image benchmark datasets. Codes are available at: https://github.com/spraise/BSNet

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