LifeLong learning （LLL）， as an emerging method， breaks the limitations of traditional machine learning and gives the models the ability to accumulate， optimize and transfer knowledge in the learning process like human beings. In recent years， with the wide application of deep learning， more and more studies attempt to solve catastrophic forgetting problem in deep neural networks and get rid of the stability-plasticity dilemma， as well as apply LLL methods to a wide varieties of real-world scenarios to promote the development of artificial intelligence from weak to strong. Aiming at the field of computer vision， firstly， LLL methods were classified into four types in image classification tasks： data-driven methods， optimization process based methods， network structure based methods and knowledge combination based methods. Then， typical applications of LLL methods in other visual tasks and related evaluation indicators were introduced. Finally， the deficiencies of LLL methods at current stage were discussed， and the future development directions of LLL methods were proposed.