Associating Multi-Scale Receptive Fields for Fine-grained Recognition

• URL: http://arxiv.org/abs/2005.09153v1
• Date: Tue, 19 May 2020 01:16:31 GMT
• Title: Associating Multi-Scale Receptive Fields for Fine-grained Recognition
• Authors: Zihan Ye, Fuyuan Hu, Yin Liu, Zhenping Xia, Fan Lyu, Pengqing Liu
• Abstract summary: We propose a novel cross-layer non-local (CNL) module to associate multi-scale receptive fields by two operations. CNL computes correlations between features of a query layer and all response layers. Our model builds spatial dependencies among multi-level layers and learns more discriminative features.
• Score: 5.079292308180334
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting and fusing part features have become the key of fined-grained image recognition. Recently, Non-local (NL) module has shown excellent improvement in image recognition. However, it lacks the mechanism to model the interactions between multi-scale part features, which is vital for fine-grained recognition. In this paper, we propose a novel cross-layer non-local (CNL) module to associate multi-scale receptive fields by two operations. First, CNL computes correlations between features of a query layer and all response layers. Second, all response features are weighted according to the correlations and are added to the query features. Due to the interactions of cross-layer features, our model builds spatial dependencies among multi-level layers and learns more discriminative features. In addition, we can reduce the aggregation cost if we set low-dimensional deep layer as query layer. Experiments are conducted to show our model achieves or surpasses state-of-the-art results on three benchmark datasets of fine-grained classification. Our codes can be found at github.com/FouriYe/CNL-ICIP2020.

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