An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamle...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12823 |
| _version_ | 1797400406807019520 |
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| author | Muhammad Hassan Tanveer Zainab Fatima Shehnila Zardari David Guerra-Zubiaga |
| author_facet | Muhammad Hassan Tanveer Zainab Fatima Shehnila Zardari David Guerra-Zubiaga |
| author_sort | Muhammad Hassan Tanveer |
| collection | DOAJ |
| description | This review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies. |
| first_indexed | 2024-03-09T01:55:07Z |
| format | Article |
| id | doaj.art-bc2d123a155147deb6e012d4b242ef15 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:07Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bc2d123a155147deb6e012d4b242ef152023-12-08T15:11:50ZengMDPI AGApplied Sciences2076-34172023-11-0113231282310.3390/app132312823An In-Depth Analysis of Domain Adaptation in Computer and Robotic VisionMuhammad Hassan Tanveer0Zainab Fatima1Shehnila Zardari2David Guerra-Zubiaga3Department of Robotics & Mechatronics Engineering, Kennesaw State University, Marietta, GA 30060, USADepartment of Software Engineering, Ned University of Engineering & Technology, Karachi 75270, PakistanDepartment of Software Engineering, Ned University of Engineering & Technology, Karachi 75270, PakistanDepartment of Robotics & Mechatronics Engineering, Kennesaw State University, Marietta, GA 30060, USAThis review article comprehensively delves into the rapidly evolving field of domain adaptation in computer and robotic vision. It offers a detailed technical analysis of the opportunities and challenges associated with this topic. Domain adaptation methods play a pivotal role in facilitating seamless knowledge transfer and enhancing the generalization capabilities of computer and robotic vision systems. Our methodology involves systematic data collection and preparation, followed by the application of diverse assessment metrics to evaluate the efficacy of domain adaptation strategies. This study assesses the effectiveness and versatility of conventional, deep learning-based, and hybrid domain adaptation techniques within the domains of computer and robotic vision. Through a cross-domain analysis, we scrutinize the performance of these approaches in different contexts, shedding light on their strengths and limitations. The findings gleaned from our evaluation of specific domains and models offer valuable insights for practical applications while reinforcing the validity of the proposed methodologies.https://www.mdpi.com/2076-3417/13/23/12823domain adaptationcomputer visionrobotic visionknowledge transfergeneralizationevaluation metrics |
| spellingShingle | Muhammad Hassan Tanveer Zainab Fatima Shehnila Zardari David Guerra-Zubiaga An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision Applied Sciences domain adaptation computer vision robotic vision knowledge transfer generalization evaluation metrics |
| title | An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision |
| title_full | An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision |
| title_fullStr | An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision |
| title_full_unstemmed | An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision |
| title_short | An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision |
| title_sort | in depth analysis of domain adaptation in computer and robotic vision |
| topic | domain adaptation computer vision robotic vision knowledge transfer generalization evaluation metrics |
| url | https://www.mdpi.com/2076-3417/13/23/12823 |
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