A comprehensive survey on deep-learning-based visual captioning

Bowen Xin, Ning Xu, Yingchen Zhai, Tingting Zhang, Zimu Lu, Jing Liu, Weizhi Nie, Xuanya Li, An-An Liu

Abstract: Generating a description for an image/video is termed as the visual captioning task. It requires the model to capture the semantic information of visual content and translate them into syntactically and semantically human language. Connecting both research communities of computer vision (CV) and natural language processing (NLP), visual captioning presents the big challenge to bridge the gap between low-level visual features and high-level language information. Thanks to recent advances in deep learning, which are widely applied to the fields of visual and language modeling, the visual captioning methods depending on the deep neural networks has demonstrated state-of-the-art performances. In this paper, we aim to present a comprehensive survey of existing deep learning-based visual captioning methods. Relying on the adopted mechanism and technique to narrow the semantic gap, we divide visual captioning methods into various groups. Representative categories in each group are summarized, and their strengths and limitations are discussed. The quantitative evaluations of state-of-the-art approaches on popular benchmark datasets are also presented and analyzed. Furthermore, we provide the discussions on future research directions.