Parsing Objects at a Finer Granularity: A Survey

作     者：Yifan Zhao Jia Li Yonghong Tian Yifan Zhao;Jia Li;Yonghong Tian

作者机构：School of Computer SciencePeking UniversityBeijing100871China State Key Laboratory of Virtual Reality Technology and SystemsSchool of Computer Science and EngineeringBeihang UniversityBeijing100191China 

出 版 物：《Machine Intelligence Research》 (机器智能研究（英文版）)

年 卷 期：2024年第21卷第3期

页      面：431-451页

核心收录：

学科分类：08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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). 

主　　题：Finer granularity visual parsing part segmentation fine-grained object recognition part relationship 

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.