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

翻译：开放集图像识别是计算机视觉中的一个具有挑战性的课题。现有文献中的大多数工作侧重于从输入图像中学习更具区分性的特征，然而，它们通常对特征中的高频或低频分量不敏感，导致在细粒度图像识别上的性能下降。为解决这一问题，我们提出了一种互补频率变化感知网络（CFAN），该网络能够更好地捕获高频和低频信息。所提CFAN由三个顺序模块组成：(i) 引入特征提取模块，用于从输入图像中学习初步特征；(ii) 设计频率变化滤波模块，通过可调频率滤波器在频域中从初步特征中分离出高低频分量；(iii) 设计互补时间聚合模块，通过两个长短期记忆网络将高低频分量聚合为判别性特征。基于CFAN，我们进一步提出一种开放集细粒度图像识别方法CFAN-OSFGR，该方法通过CFAN学习图像特征，并通过线性分类器进行分类。在3个细粒度数据集和2个粗粒度数据集上的实验结果表明，CFAN-OSFGR在大多数情况下显著优于9种现有最优方法。