Artificial Intelligence for Smart Image Perception, Recognition and Understanding
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 12
Special Issue Editors
Interests: deep neural networks; image analysis and understanding; video action understanding; human pose estimation; applications of large foundation models; concept editing on generative models; cross-modal content alignment; multi-modal fusion
Interests: multimodal content analysis; model lightweighting; multimodal large models
Special Issue Information
Dear Colleagues,
This Special Issue focuses on smart, data-efficient, and trustworthy image understanding across tasks such as recognition, segmentation, VQA, captioning, generation, and cross-modal alignment. We welcome contributions spanning theoretical advances to practical systems, covering (but not limited to) deep architecture design, self-/un-/weak supervised training, vision–language models, generative–discriminative integration, 2D–3D geometric reasoning, efficiency strategies, robustness and uncertainty, and benchmarking and reproducibility. Application areas include media data processing, medical imaging, industrial inspection, autonomous driving, and remote sensing, among others. By emphasizing shared architectures, data-efficient training paradigms, and rigorous evaluation protocols, this Special Issue aims to transcend task-specific silos and accelerate the development of practical, reliable, and transparent image understanding technologies.
Prof. Dr. Yanbin Hao
Dr. Shuo Wang
Dr. Jinmeng Wu
Guest Editors
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Keywords
- image classification/recognition/retrieval/detection
- deep image neural networks
- vision–language models
- image generative models
- open-vocabulary detection/segmentation
- image processing/augmentation
- self/un/weakly/full supervised learning
- instruction tuning and prompting for vision
- 2D–3D fusion and depth estimation
- anomaly/defect detection
- cross-modal retrieval and alignment
- human pose estimation
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