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Open-set image recognition is a challenging topic in computer vision. Most ofthe existing works in literature focus on learning more discriminative featuresfrom the input images, however, they are usually insensitive to the high- orlow-frequency components in features, resulting in a decreasing performance onfine-grained image recognition. To address this problem, we propose aComplementary Frequency-varying Awareness Network that could better captureboth high-frequency and low-frequency information, called CFAN. The proposedCFAN consists of three sequential modules: (i) a feature extraction module isintroduced for learning preliminary features from the input images; (ii) afrequency-varying filtering module is designed to separate out both high- andlow-frequency components from the preliminary features in the frequency domainvia a frequency-adjustable filter; (iii) a complementary temporal aggregationmodule is designed for aggregating the high- and low-frequency components viatwo Long Short-Term Memory networks into discriminative features. Based onCFAN, we further propose an open-set fine-grained image recognition method,called CFAN-OSFGR, which learns image features via CFAN and classifies them viaa linear classifier. Experimental results on 3 fine-grained datasets and 2coarse-grained datasets demonstrate that CFAN-OSFGR performs significantlybetter than 9 state-of-the-art methods in most cases.

Complementary Frequency-Varying Awareness Network for Open-Set Fine-Grained Image Recognition