Abstract
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.
Abstract (translated)
开放集图像识别是计算机视觉中的挑战性话题。现有的文献大多数集中在从输入图像中学习更敏感的特征,然而,它们通常对特征中的高频或低频成分不敏感,导致在精细图像识别方面的性能下降。为了解决这一问题,我们提出了一种互补的频率可变感知网络,称为CFAN,它能够更好地捕捉高频和低频信息。CFAN由三个Sequential模块组成:(i)引入特征提取模块,以从输入图像中学习初步特征;(ii)设计一个频率可变滤波器模块,通过一个可调整滤波器从频率域中分离出高和低频成分,并将它们与初步特征一起聚合成精细特征;(iii)设计一个互补的时间聚合模块,以通过两个长期短期记忆网络将高和低频成分聚合成精细特征。基于CFAN,我们提出了一种开放集精细图像识别方法,称为CFAN-OSFGR,它通过CFAN学习图像特征,并通过线性分类器进行分类。对三个精细数据集和两个粗粒度数据集的实验结果表明,CFAN-OSFGR在大多数情况下比9个最先进的方法表现得更好。
URL
https://arxiv.org/abs/2307.07214
