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

Named Entity Recognition (NER) frequently suffers from the problem of
insufficient labeled data, particularly in fine-grained NER scenarios. Although
$K$-shot learning techniques can be applied, their performance tends to
saturate when the number of annotations exceeds several tens of labels. To
overcome this problem, we utilize existing coarse-grained datasets that offer a
large number of annotations. A straightforward approach to address this problem
is pre-finetuning, which employs coarse-grained data for representation
learning. However, it cannot directly utilize the relationships between
fine-grained and coarse-grained entities, although a fine-grained entity type
is likely to be a subcategory of a coarse-grained entity type. We propose a
fine-grained NER model with a Fine-to-Coarse(F2C) mapping matrix to leverage
the hierarchical structure explicitly. In addition, we present an inconsistency
filtering method to eliminate coarse-grained entities that are inconsistent
with fine-grained entity types to avoid performance degradation. Our
experimental results show that our method outperforms both $K$-shot learning
and supervised learning methods when dealing with a small number of
fine-grained annotations.

利用现有粗粒度数据集进行预精调，通过细粒度实体类型与粗粒度实体类型之间的映射矩阵以及不一致过滤方法，解决细粒度命名实体识别缺少标注数据的问题，并在实验中证明在处理少量细粒度标注时，该方法优于 $K$-shot 学习和有监督学习方法。