{"title":"A Survey of Visual Affordance Recognition Based on Deep Learning","authors":"Dongpan Chen;Dehui Kong;Jinghua Li;Shaofan Wang;Baocai Yin","doi":"10.1109/TBDATA.2023.3291558","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 6","pages":"1458-1476"},"PeriodicalIF":7.5000,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Big Data","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10171410/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 2
Abstract
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.
期刊介绍:
The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.