摘要
Semantic image parsing, which refers to the pro- cess of decomposing images into semantic regions and constructing the structure representation of the input, has re- cently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.
Semantic image parsing, which refers to the pro- cess of decomposing images into semantic regions and constructing the structure representation of the input, has re- cently aroused widespread interest in the field of computer vision. The recent application of deep representation learning has driven this field into a new stage of development. In this paper, we summarize three aspects of the progress of research on semantic image parsing, i.e., category-level semantic segmentation, instance-level semantic segmentation, and beyond segmentation. Specifically, we first review the general frameworks for each task and introduce the relevant variants. The advantages and limitations of each method are also discussed. Moreover, we present a comprehensive comparison of different benchmark datasets and evaluation metrics. Finally, we explore the future trends and challenges of semantic image parsing.
基金
This work was supported by the National Science Fund for Excellent Young Scholars （61622214）, the National Natural Science Foundation of China （Grant Nos. 61702565 and 61622214）, Guangdong Natural Science Foundation Project for Research Teams （2017A030312006）, and was also sponsored by CCF-Tencent Open Research Fund.
