From Global to Hybrid: A Review of Supervised Deep Learning for 2-D Image Feature Representation

作     者：Dong, Xinyu Wang, Qi Deng, Hongyu Yang, Zhenguo Ruan, Weijian Liu, Wu Lei, Liang Wu, Xue Tian, Youliang 

作者机构：Guizhou University State Key Laboratory of Public Big Data College of Computer Science Guiyang550025 China Guangdong University of Technology Guangzhou510006 China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China AI Research of *** Beijing100101 China 

出 版 物：《IEEE Transactions on Artificial Intelligence》 (IEEE. Trans. Artif. Intell.)

年 卷 期：2025年第6卷第6期

页      面：1540-1560页

核心收录：

基　　金：This research was supported by the National Key R&D Program of China under Grant 2024YFD2001100  and Grant 2024YFE0214300  by the National Natural Science Foundation of China under Grant 62162008  by the Guizhou Provincial Science and Technology Projects (002  CXTD027)  by the Guizhou Province Youth Science and Technology Talent Project (317)  and by the Guiyang Guian Science and Technology Talent Training Project ( 2-15) 

主　　题：Self supervised learning 

摘      要：Computer vision is the science that aims to enable computers to emulate human visual perception, and it encompasses various techniques and methods for extracting and interpreting information from two-dimensional images. Supervised deep 2-D image feature representation is a fundamental problem in computer vision that applies deep learning techniques to extract and process information from a given 2-D image under supervised settings. The goal is to obtain a feature vector that can be utilized for various downstream computer vision applications. The quality of supervised deep 2-D image feature representation algorithms directly affects the performance of downstream applications. However, most of the existing vision research only explores supervised deep 2-D image feature representation for specific subtasks. Therefore, a comprehensive discussion on this topic is needed. In this article, we propose a taxonomy of supervised deep 2-D image feature representation methods based on four categories: global representation, region representation, hash representation, and hybrid representation, and we introduce their typical approaches. Furthermore, we perform a comparative analysis of the representative methods on three fundamental tasks: image classification, object detection, and semantic segmentation, as well as other common tasks. We also discuss the limitations of supervised deep 2-D image feature representation and investigate future directions in image representation to facilitate the advancement of computer vision through image representation. © 2020 IEEE.