Open-set image recognition (OSR) aims to both classify known-class samples
and identify unknown-class samples in the testing set, which supports robust
classifiers in many realistic applications, such as autonomous driving, medical
diagnosis, security monitoring, etc. In recent years, open-set recognition
methods have achieved more and more attention, since it is usually difficult to
obtain holistic information about the open world for model training. In this
paper, we aim to summarize the up-to-date development of recent OSR methods,
considering their rapid development in recent two or three years. Specifically,
we firstly introduce a new taxonomy, under which we comprehensively review the
existing DNN-based OSR methods. Then, we compare the performances of some
typical and state-of-the-art OSR methods on both coarse-grained datasets and
fine-grained datasets under both standard-dataset setting and cross-dataset
setting, and further give the analysis of the comparison. Finally, we discuss
some open issues and possible future directions in this community.

本文总结了最新的开放式图像识别方法的发展，主要包括了基于深度神经网络的方法在粗粒度和细粒度数据集上的性能比较以及对比分析，同时讨论了该领域的若干开放问题和未来可能的研究方向。

开放集图像识别综述

A Survey on Open-Set Image Recognition

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.

提出了一种名为 CFAN 的频率可变意识网络，该网络包含三个顺序模块，其中频率可变过滤模块用于从初步特征中分离出高频和低频成分，以及互补的时间聚合模块用于通过两个 LSTM 网络汇聚高频和低频成分，实现了开放式精细图像识别，与大多数现有技术相比，在 3 个精细数据集和 2 个粗粒度数据集上表现显著优于 9 种最先进的方法。