Parsing Objects at a Finer Granularity: A Survey  

作　　者：Yifan Zhao Jia Li Yonghong Tian 

机构地区：[1]School of Computer Science,Peking University,Beijing,100871,China [2]State Key Laboratory of Virtual Reality Technology and Systems,School of Computer Science and Engineering,Beihang University,Beijing,100191,China

出　　处：《Machine Intelligence Research》2024年第3期431-451,共21页机器智能研究（英文版）

基　　金：supported in part by National Natural Science Foundation of China(Nos.62132002,61825101 and 62202010);the Key-Area Research and Development Program of Guangdong Province,China(No.2021B0101400002);the China Postdoctoral Science Foundation(No.2022M710212).

摘　　要：Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

关 键 词：Finer granularity visual parsing part segmentation fine-grained object recognition part relationship 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程]