Symmetry and Asymmetry in Deep Learning for Computer Vision
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 1 August 2026 | Viewed by 44
Special Issue Editor
Interests: machine learning; data science in agricultural system; food detection; food Analysis; artificial intelligence (AI) for agriculture; computer vision; IoT for agriculture; non-destructive evaluation for agricultural product; post-harvest machinery
Special Issue Information
Dear Colleagues,
Deep learning has revolutionized computer vision, enabling machines to perceive, analyze, and understand visual data with unprecedented accuracy. Applications range from image classification, object detection, and scene segmentation to medical imaging, agriculture, industrial inspection, and autonomous systems. Despite this rapid progress, fundamental concepts such as symmetry and asymmetry—which are deeply rooted in mathematics, physics, and human perception—have not been thoroughly investigated in the context of deep learning for vision. Symmetry offers valuable insights into invariance, equivariance, and structural regularities, while asymmetry often provides essential cues for anomaly detection, irregular patterns, or directional features.
This Special Issue, “Symmetry and Asymmetry in Deep Learning for Computer Vision”, aims to explore theoretical advances, methodological innovations, and practical applications that address the roles of symmetry and asymmetry in deep learning. It seeks to attract high-quality original research and review papers that expand our understanding of how these concepts can be leveraged to improve model robustness, interpretability, and efficiency.
Research areas may include (but are not limited to) the following:
- Symmetry-aware architectures and equivariant neural networks;
- Asymmetry modeling for anomaly detection and adversarial robustness;
- Geometric deep learning and graph-based approaches;
- Symmetry-inspired data augmentation and regularization methods;
- Hybrid frameworks combining symmetric and asymmetric features;
- Applications in medical imaging (tumor detection, organ segmentation);
- Applications in agriculture (fruit quality assessment, plant disease recognition);
- Applications in autonomous driving (road scene understanding, obstacle detection);
- Applications in industrial inspection (defect recognition, quality control);
- Applications in remote sensing (land-use classification, environmental monitoring).
I warmly invite researchers to contribute their latest work to this Special Issue, whether they present theoretical explorations, methodological advancements, or innovative real-world implementations.
I look forward to receiving your contributions.
Dr. Ravipat Lapcharoensuk
Guest Editor
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. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- symmetry
- asymmetry
- deep learning
- computer vision
- medical imaging
- precision agriculture
- autonomous driving
- industrial inspection
- remote sensing
- anomaly detection
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