As a cross-domain research topic related to Computer Vision, Multimedia, Artificial Intelligence and Natural Language Processing, the task of visual scene description is to produce automatically one or more sentences to describe the content of visual scene from an image or a video snippet. The richness of the content in the visual scene and the diversity of the expression of natural language make visual scene description a challenging task. This paper gives a review about the generation methods and performance evaluation on the recently developed visual scene description methods. Specifically, the research object and main tasks of visual scene description are firstly defined; the relationships between visual scene description and multi-modal retrieval, cross-modal learning, scene classification, visual relationship detection and other related technologies are discussed sequentially. And then, main methods and research progress of visual scene description are summarized in three categories, while the increasing benchmark datasets are discussed. Besides, some widely-used evaluation metrics and the corresponding challenges on the visual scene description are discussed. Finally, some potential applications in future are suggested.