• 综合
  • 标题
  • 关键词
  • 摘要
  • 学者
  • 期刊-刊名
  • 期刊-ISSN
  • 会议名称
搜索

作者:

Chen, Dongpan (Chen, Dongpan.) | Kong, Dehui (Kong, Dehui.) (学者:孔德慧) | Li, Jinghua (Li, Jinghua.) | Wang, Shaofan (Wang, Shaofan.) | Yin, Baocai (Yin, Baocai.)

收录:

EI Scopus SCIE

摘要:

Visual affordance recognition is an important research topic in robotics, human-computer interaction, and other computer vision tasks. In recent years, deep learning-based affordance recognition methods have achieved remarkable performance. However, there is no unified and intensive survey of these methods up to now. Therefore, this article reviews and investigates existing deep learning-based affordance recognition methods from a comprehensive perspective, hoping to pursue greater acceleration in this research domain. Specifically, this article first classifies affordance recognition into five tasks, delves into the methodologies of each task, and explores their rationales and essential relations. Second, several representative affordance recognition datasets are investigated carefully. Third, based on these datasets, this article provides a comprehensive performance comparison and analysis of the current affordance recognition methods, reporting the results of different methods on the same datasets and the results of each method on different datasets. Finally, this article summarizes the progress of affordance recognition, outlines the existing difficulties and provides corresponding solutions, and discusses its future application trends.

关键词:

function understanding Big Data Image segmentation computer vision deep learning models Task analysis Surveys Visual affordance recognition Feature extraction robotics convolutional neural network Humanities Affordances

作者机构:

  • [ 1 ] [Chen, Dongpan]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 2 ] [Kong, Dehui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Jinghua]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 4 ] [Wang, Shaofan]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
  • [ 5 ] [Yin, Baocai]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China

通讯作者信息:

查看成果更多字段

相关关键词:

相关文章:

来源 :

IEEE TRANSACTIONS ON BIG DATA

ISSN: 2332-7790

年份: 2023

期: 6

卷: 9

页码: 1458-1476

7 . 2 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 2

SCOPUS被引频次: 7

ESI高被引论文在榜: 0 展开所有

万方被引频次:

中文被引频次:

近30日浏览量: 2

归属院系:

在线人数/总访问数:559/4956184
地址:北京工业大学图书馆(北京市朝阳区平乐园100号 邮编:100124) 联系我们:010-67392185
版权所有:北京工业大学图书馆 站点建设与维护:北京爱琴海乐之技术有限公司