Algorithms and Models for Next-Generation Vision Systems
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 20 April 2026 | Viewed by 76
Special Issue Editors
Interests: computer vision; multi-modal image fusion; deep learning; reinforcement learning
Interests: computer vision; connected vehicle control; intelligentization of transportation vehicles for mining operations; universal mining robotics
Interests: computer vision; brain–computer interfaces; deep learning; neural signal processing
Interests: signal processing; brain–computer interfaces; human–machine interaction; deep learning; rehabilitation robotics
Interests: image processing; neurofeedback; machine learning; brain–computer interfaces
Special Issue Information
Dear Colleagues,
The rapid advancement of artificial intelligence and computational hardware has paved the way for next-generation vision systems that leverage cutting-edge models and algorithms, such as convolutional neural networks, attention mechanisms, and transformer-based architectures, to achieve superior accuracy, adaptability, and real-time performance. These advancements, further enriched through the use of multi-modal and self-supervised learning and lightweight model optimization techniques, enable vision systems to operate reliably in increasingly complex and dynamic environments.
The integration of next-generation vision systems with the future Internet, characterized by connectivity, edge and cloud computing, the Internet of Things (IoT), and intelligent cyber–physical systems, is reshaping the development and deployment of visual intelligence. Vision models can now be distributed across devices and infrastructures, enabling collaborative perception, real-time decision-making, and intelligent interaction in networked environments. This integration facilitates the development of scalable, context-aware, and low-latency visual systems, essential in applications such as autonomous driving, smart manufacturing, remote healthcare, and intelligent surveillance.
For this Special Issue, we are seeking original, unpublished contributions focusing on novel algorithms, model architectures, and intelligent systems for next-generation vision applications. Authors are encouraged to submit papers on theoretical innovations, applied research, and system-level developments that address real-world challenges. We also welcome comprehensive reviews and surveys. The topics of interest include, but are not limited to, the following:
- Vision transformer and attention-based models;
- Lightweight and efficient vision models for edge deployment;
- Multi-modal learning and sensor fusion in visual systems;
- Adaptive vision systems in dynamic or uncertain environments;
- Real-time object detection, tracking, and scene understanding;
- Self-supervised and few-shot learning in vision tasks;
- Interpretability, explainability, and trust in vision models;
- Vision system applications in autonomous driving, robotics, healthcare, and manufacturing.
Dr. Daqi Jiang
Dr. Jian Pan
Dr. Fo Hu
Dr. Yanzheng Lu
Dr. Naishi Feng
Dr. Bin Zhou
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- deep learning for vision
- vision transformers and attention mechanisms
- multi-modal sensor fusion
- lightweight and edge vision models
- self-supervised and few-shot learning
- scene understanding and object detection
- AI for autonomous systems and robotics
- explainable and trustworthy vision AI
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