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Symmetry
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Symmetry/Asymmetry in Computer Vision and Artificial Intelligence
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Symmetry/Asymmetry in Computer Vision and Artificial Intelligence

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 1258

Special Issue Editors

Prof. Dr. Diana-Margarita Córdova-Esparza

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Guest Editor
Facultad de Informática, Universidad Autónoma de Querétaro, Av. de las Ciencias S/N, Juriquilla, Querétaro 76230, Mexico
Interests: artificial intelligence; artificial vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Manuel Álvarez-Alvarado

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Guest Editor
Facultad de Ingeniería, Universidad Autónoma de Queretaro, Queretaro 76010, Mexico
Interests: artificial intelligence; embedded systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan R. Terven

E-Mail Website
Guest Editor
Instituto Politécnico Nacional, CICATA-Qro, Queretaro 76090, Mexico
Interests: modern vision using deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Symmetry and asymmetry are fundamental concepts in computer vision and artificial intelligence (AI) that play an essential role in the analysis and interpretation of complex data. The principles of symmetry have been instrumental in advancing areas such as image recognition, 3D object reconstruction, and neural network architectures, where symmetrical structures can lead to more efficient computation and improved accuracy. However, real-world data often present asymmetrical challenges, from uneven data distributions to irregular object shapes, requiring sophisticated AI approaches to handle these complexities.

Recent advancements in AI and computer vision are pushing the boundaries of what can be achieved in various domains, including robotics, medical imaging, autonomous navigation, and even education. This Special Issue invites contributions that explore the use of symmetry and asymmetry in AI models, algorithms, and applications, encouraging the integration of these concepts to address key challenges in the field. Researchers from computer vision, AI, robotics, and related fields are encouraged to submit their findings.

Prof. Dr. Diana-Margarita Córdova-Esparza
Prof. Dr. José Manuel Álvarez-Alvarado
Prof. Dr. Juan R. Terven
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • symmetry in AI
  • asymmetry in computer vision
  • neural networks
  • 3D object reconstruction
  • image recognition
  • AI in education
  • robotics and AI
  • data augmentation
  • adaptive learning algorithms
  • bias and fairness in AI
  • pattern recognition in complex environments
  • statistics and machine learning for vision
  • model-based vision
  • natural language processing
  • speech processing
  • inclusive technology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

16 pages, 3272 KB  
Article
Enhancing Fairness Without Demographic Labels via Identifying and Mitigating Potential Biases
by Pilhyeon Lee and Sungho Park
Symmetry 2026, 18(2), 344; https://doi.org/10.3390/sym18020344 - 12 Feb 2026
Viewed by 456
Abstract
Asymmetries in data distributions and performance across subgroups can induce systematic unfairness in real-world systems. A variety of previous studies have significantly ameliorated the fairness of deep learning models; however, most of them necessarily require additional labels for sensitive attributes, (i.e., ethnicity and [...] Read more.
Asymmetries in data distributions and performance across subgroups can induce systematic unfairness in real-world systems. A variety of previous studies have significantly ameliorated the fairness of deep learning models; however, most of them necessarily require additional labels for sensitive attributes, (i.e., ethnicity and gender). Since sensitive attributes often correspond to personal information, collecting such labels can be restricted and may raise privacy concerns. Although recent work has sought to address these issues by training a model without sensitive attribute labels, we point out that it has limitations, as it assumes specific characteristics of sensitive attributes and is validated in simplistic, constrained environments. Therefore, we propose an Unsupervised Fairness-aware Framework (UFF) that trains a fair classification model without pre-defining the characteristics of the sensitive attributes. It includes branches that capture various types of biases and eliminates them through adversarial training. In various scenarios on benchmark datasets, (i.e., CelebA and UTK Face) for facial attribute classification, the proposed method significantly enhances fairness without assuming specific characteristics of sensitive attributes. Moreover, we introduce g-FAT, which is a new metric to measure generalized trade-off performances between classification accuracy and fairness. For example, on CelebA, ours reduces EO from 11.8 to 7.6 for malignant bias and from 15.6 to 9.6 for benign bias, while improving g-FAT from 80.7 to 84.9 and from 79.0 to 85.2, respectively. In terms of g-FAT, our method achieves the highest trade-off performance among the compared methods on the benchmarks. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Computer Vision and Artificial Intelligence)
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