Multimedia Tools and Applications - Call for Papers: Multi-modal Information Analysis and Applications based on Chat-GPT [1243]
With the emergence of ChatGPT and the release of a series of extended products and functional plug-ins, its excellent performance and summary analysis capabilities have left a deep impression on scientific researchers, and have also influenced research ideas and research methods in some traditional research fields, such as recommendation, emotion recognition, information generation, and VQA. Part of the work has been improved with the support of ChatGPT, especially in the field of data generation. ChatGPT can provide effective additional information to improve the quality of data generation.
The use of ChatGPT and other large models remains a research hotspot in the field of multimedia analysis and application. However, there are several key issues that need to be addressed: 1) What kind of interaction method is used to get more effective auxiliary information from ChatGPT; 2) How to combine with traditional questions to give full play to the greater advantages of ChatGPT; 3) How to process the information provided by ChatGPT and integrate it with the target data. Especially in the field of AIGC, a major challenge is how to efficiently leverage prior knowledge from large models and explore consistency and complementary properties across different modalities to enhance multi-modal generation performance.
The goal of this special issue in the Journal of MTAP is to collect high-quality articles that concentrate on developments, trends, and research solutions in cross-modal and multi-modal information representation and generation utilizing ChatGPT. The topics of interest include, but are not limited to:
- Multi-modal information generation
- 3D model generation and reconstruction
- Multi-modal-data based real-world applications, e.g., object detection/tracking, image segmentation, video understanding/categorization, scene understanding, action recognition, classification/clustering tasks, etc.
- Advanced deep Learning techniques for multi-modal data learning and understanding.
- Structured/semi-structured multi-modal data learning (e.g., one-shot, zero-shot, supervised, and semi-/unsupervised learning).
- Multi-task/Transfer learning for multi-view data understanding. Multi-modal-data based medical applications (diagnosis, reconstruction, segmentation, registration, etc.)
- Multi-modal-data based medical image analysis and diagnosis generation.
- Survey papers with regards to topics of multi-model representation learning and generation.
- New benchmark datasets collection for multi-modal data learning.
Important Dates:
Submissions Open: June 1, 2023
Submission Deadline: October 1, 2023
Guest Editors:
Jie Nie - Ocean University of China
Email: niejie@ouc.edu.cn
Boyu Wang - University of Western Ontario, Canada
Email: bwang@csd.uwo.ca
Yuxin Ma - Southern University of Science and Technology, China
Email: mayx@sustech.edu.cn
Weizhi Nie - Tianjin University, China
Email: weizhinie@tju.edu.cn
Submission Guidelines:
Authors should prepare their manuscript according to the Instructions for Authors available from the Multimedia Tools and Applications website (this opens in a new tab). Authors should submit through the online submission site at https://www.editorialmanager.com/mtap/default2.aspx (this opens in a new tab) and select “SI 1243- Multi-modal Information Analysis and Applications based on Chat-GPT" when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Final decisions on all papers are made by the Editor in Chief.