Fine-Grained Visual Understanding with Vision Language Models
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 December 2026 | Viewed by 222
Editors
Interests: action recognition; anomaly detection; video image processing; one- and few-shot learning; deep learning; tensor learning; domain adaptation
Special Issues, Collections and Topics in MDPI journals
Interests: time series analysis; anomaly detection; machine learning; deep learning; large language models (LLMs); embedded systems; edge device integration; power system analysis
Special Issue Information
Dear Colleagues,
Recent progress in vision language models has substantially advanced visual understanding by connecting perception with semantic reasoning. Despite these developments, fine-grained and ultra-fine-grained understanding remains a core challenge, requiring models to capture subtle visual differences, detailed attributes, and context-dependent semantics. This Special Issue focuses on new directions that enable precise and scalable visual understanding beyond coarse categorization.
We invite contributions that investigate how large-scale pretraining, multimodal representation learning, and structured reasoning can improve fine-grained recognition and interpretation. Relevant topics include fine-grained classification, attribute-level understanding, vision language alignment, open vocabulary recognition, and compositional reasoning. We also encourage work on data-efficient learning, robustness under distribution shifts, and interpretable models that provide insight into decision processes.
This Special Issue aims to bring together advances across computer vision and multimodal learning to define the next stage of visual understanding, where models achieve both high precision and strong generalization in complex real-world scenarios.
Dr. Lei Wang
Dr. Zicheng Pan
Guest Editors
Manuscript Submission Information
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Keywords
- fine-grained visual recognition
- ultra-fine-grained understanding
- vision language models
- multimodal learning
- cross-modal alignment
- open vocabulary recognition
- compositional reasoning
- visual attribute learning
- interpretable models
- robustness
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