[Journal] [Special Issue] CFP: Image and Vision Computing
Image and Vision Computing CALL FOR PAPERS Special Issue on Advances in Domain Adaptation for Computer Vision Aim and Scope: In daily routines, humans, not only learn and apply knowledge for visual tasks but also have intrinsic abilities to transfer knowledge between related vision tasks. For example, if a new vision task is relevant to any previous learning, it is possible to transfer the learned knowledge for handling the new vision task. In developing new computer vision algorithms, it is desired to utilize these capabilities to make the algorithms adaptable. Generally, traditional computer vision methods do not adapt to a new task and have to learn the new task from the beginning. These methods do not consider that the two visual tasks may be related and the knowledge gained in one may be applied to learn the other one efficiently in lesser time. Domain adaptation for computer vision is the area of research, which attempts to mimic this human behavior by transferring the knowledge learned in one or more source domains and use it for learning the related visual processing task in the target domain. Recent advances in domain adaptation, particularly in cotraining, transfer learning, and online learning have benefited computer vision research significantly. For example, learning from high-resolution source domain images and transferring the knowledge to learning low-resolution target domain information. This special issue will focus on the recent advances in domain adaptation for different computer vision tasks. Topics of interest include, but are not limited to: · Domain adaptation for machine learning frameworks for learning deep representations · Domain adaptation for face detection/recognition and tracking · Domain adaptation for object detection/ recognition and tracking · Domain adaptation and hybrid models for real-time computer vision tasks · Domain adaptation for human pose detection/recognition and estimation · Domain adaptation for event/action detection and recognition · Domain adaptation for few-shot learning · Domain adaptation for deep neural network optimization Important Dates: Paper submission due: May 31, 2020 First notification: July 31, 2020 Revision submission due: September 30, 2020 Final decision: November 30, 2020 Paper evaluation and submission: Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, the significance of contribution, technical quality, and quality of presentation, by at least two independent reviewers (the papers will be reviewed following standard peer-review procedures of the Journal). Each paper will be reviewed rigorously and possibly in two rounds. Prospective authors should follow the formatting and Instructions of Image and Vision Computing at https://www.elsevier.com/journals/image-and-vision-computing/0262-8856/guide..., and invited to submit their papers directly via the online submission system at https://www.editorialmanager.com/IMAVIS/default.aspx. When submitting your manuscript please select the article type "VSI: Advances in Domain Adaptation for Computer Vision (ADACV)" Please submit your manuscript before the submission deadline. [ https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers... | https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers... ] Guest Editors: Dr. Pourya Shamsolmoali Institute of Image Processing & Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. Email: [ mailto:pshams@sjtu.edu.cn | pshams@sjtu.edu.cn ] Prof. Salvador Garcaí Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain. Email: [ mailto:salvagl@decsai.ugr.es | salvagl@decsai.ugr.es ] Dr. Huiyu Zhou Department of Informatics, University of Leicester, Leicester, UK. Email: [ mailto:hz143@leicester.ac.uk | hz143@leicester.ac.uk ] Prof. M. Emre Celebi Department of Computer Science, University of Central Arkansas, Conway, Arkansas, USA. Email: [ mailto:ecelebi@uca.edu | ecelebi@uca.edu ]
Image and Vision Computing CALL FOR PAPERS Special Issue on Advances in Domain Adaptation for Computer Vision Aim and Scope: In daily routines, humans, not only learn and apply knowledge for visual tasks but also have intrinsic abilities to transfer knowledge between related vision tasks. For example, if a new vision task is relevant to any previous learning, it is possible to transfer the learned knowledge for handling the new vision task. In developing new computer vision algorithms, it is desired to utilize these capabilities to make the algorithms adaptable. Generally, traditional computer vision methods do not adapt to a new task and have to learn the new task from the beginning. These methods do not consider that the two visual tasks may be related and the knowledge gained in one may be applied to learn the other one efficiently in lesser time. Domain adaptation for computer vision is the area of research, which attempts to mimic this human behavior by transferring the knowledge learned in one or more source domains and use it for learning the related visual processing task in the target domain. Recent advances in domain adaptation, particularly in cotraining, transfer learning, and online learning have benefited computer vision research significantly. For example, learning from high-resolution source domain images and transferring the knowledge to learning low-resolution target domain information. This special issue will focus on the recent advances in domain adaptation for different computer vision tasks. Topics of interest include, but are not limited to: · Domain adaptation for machine learning frameworks for learning deep representations · Domain adaptation for face detection/recognition and tracking · Domain adaptation for object detection/ recognition and tracking · Domain adaptation and hybrid models for real-time computer vision tasks · Domain adaptation for human pose detection/recognition and estimation · Domain adaptation for event/action detection and recognition · Domain adaptation for few-shot learning · Domain adaptation for deep neural network optimization Important Dates: Paper submission due: May 31, 2020 First notification: July 31, 2020 Revision submission due: September 30, 2020 Final decision: November 30, 2020 Paper evaluation and submission: Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, the significance of contribution, technical quality, and quality of presentation, by at least two independent reviewers (the papers will be reviewed following standard peer-review procedures of the Journal). Each paper will be reviewed rigorously and possibly in two rounds. Prospective authors should follow the formatting and Instructions of Image and Vision Computing at https://www.elsevier.com/journals/image-and-vision-computing/0262-8856/guide..., and invited to submit their papers directly via the online submission system at https://www.editorialmanager.com/IMAVIS/default.aspx. When submitting your manuscript please select the article type "VSI: Advances in Domain Adaptation for Computer Vision (ADACV)" Please submit your manuscript before the submission deadline. [ https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers... | https://www.journals.elsevier.com/image-and-vision-computing/call-for-papers... ] Guest Editors: Dr. Pourya Shamsolmoali Institute of Image Processing & Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. Email: [ mailto:pshams@sjtu.edu.cn | pshams@sjtu.edu.cn ] Prof. Salvador Garcaí Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain. Email: [ mailto:salvagl@decsai.ugr.es | salvagl@decsai.ugr.es ] Dr. Huiyu Zhou Department of Informatics, University of Leicester, Leicester, UK. Email: [ mailto:hz143@leicester.ac.uk | hz143@leicester.ac.uk ] Prof. M. Emre Celebi Department of Computer Science, University of Central Arkansas, Conway, Arkansas, USA. Email: [ mailto:ecelebi@uca.edu | ecelebi@uca.edu ]
participants (1)
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Pourya Shamsolmoali