With the fast growing number of images,especially the user-generated ones,the semantic content of images become richer,and labels become more complex.Therefore,the study on image multi-label learning is one of the hot research areas in both academia and industry,and a large number of efficient methods have emerged in recent years.This paper surveys the existing work on image multi-label learning in recent years.Firstly,we briefly describe the concept of multi-label learning and introduce two types of methods,that is,single-instance multi-label learning and multi-instance multi-label learning.Then,we summarize three challenges on multi-label learning caused by the big data characteristics,and provide related work which can handle these challenges.Finally,we elaborate two applications on image recognition and automatic drive to show that multi-label learning techniques can be effective for many application scenarios.