Vision-Based AI in the Real World: Data, Robustness and Deployment
A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".
Deadline for manuscript submissions: 31 July 2027 | Viewed by 1318
Editors
Interests: machine learning; generative AI; multimodal AI; knowledge-enhanced AI; AI for healthcare; computer vision; digital health; health informatics
Interests: pattern recognition; bioinformatics; multi-omics data analysis; image genetics; multimodal brain imaging; neurodegenerative diseases; cancer analysis
Interests: computer vision; video analytics; medical imaging; multi-agent systems; open-world learning; concept drift
Special Issue Information
Dear Colleagues,
Recent advances in vision-based artificial intelligence have led to significant progress in visual recognition tasks under controlled settings. However, deploying these models in real-world environments remains challenging due to the complex, dynamic, and often imperfect nature of real-world data. Issues such as data heterogeneity, distribution shift, limited annotations, and long-tail scenarios can significantly degrade model performance outside laboratory conditions.
In response, there is a growing shift toward data-centric approaches, emphasizing the critical role of data quality, curation, and governance in building reliable vision systems. At the same time, robustness and generalization have become central concerns, requiring models that can adapt to unseen conditions and maintain performance across diverse environments. Furthermore, the practical deployment of vision-based AI systems raises additional challenges related to scalability, efficiency, and integration into real-world workflows.
This Special Issue aims to bring together recent advances at the intersection of data-centric AI, robust visual recognition, and real-world deployment. We welcome contributions addressing challenges such as dataset construction and curation, weak and noisy supervision, domain adaptation, multimodal data integration, and real-world evaluation protocols. Applications may span healthcare, industrial inspection, autonomous systems, and other real-world scenarios.
By focusing on the interplay between data, robustness, and deployment, this Special Issue seeks to bridge the gap between theoretical advances and practical, real-world vision systems.
Dr. Daqian Shi
Prof. Dr. Wei Kong
Dr. Yuqi Ouyang
Guest Editors
Dr. Haonan Zhao
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- real-world vision AI
- data-centric AI
- robust visual recognition
- domain generalization
- out-of-distribution robustness
- multimodal learning
- trustworthy AI
- foundation models
- vision-language models
- real-world deployment
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