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Mathematics
Special Issues
Computational Intelligence, Computer Vision and Pattern Recognition
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Computational Intelligence, Computer Vision and Pattern Recognition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 861

Special Issue Editor

Dr. Jian-Fang Hu

E-Mail Website
Guest Editor
1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
2. Guangdong Province Key Laboratory of Information Security Technology, Guangzhou, China
3. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Beijing, China
Interests: computer vision; 3D human video prediction and generation; multimodal video understanding; multimodal large models; ocean large models; trajectory prediction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled “Computational Intelligence, Computer Vision and Pattern Recognition” aims to provide a comprehensive overview of the latest research and innovations in the application of computer vision and pattern recognition techniques. It seeks to highlight how these technologies are being used to address real-world challenges across various domains such as healthcare, autonomous systems, security, and robotics. We invite authors to submit original research that explores both theoretical advancements and practical applications, with a focus on areas such as image and video analysis, object detection, recognition systems, deep learning methods, and multimodal learning. The goal of this Special Issue is to offer a platform for researchers to present novel solutions, discuss emerging trends, and showcase the transformative potential of computer vision and pattern recognition in solving complex, real-world problems.

Dr. Jian-Fang Hu
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • computer vision
  • pattern recognition
  • deep learning
  • image analysis
  • object detection
  • recognition systems
  • multimodal learning
  • video analysis

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Published Papers (1 paper)

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Research

15 pages, 4930 KB  
Article
A Lightweight Hybrid CNN-ViT Network for Weed Recognition in Paddy Fields
by Tonglai Liu, Yixuan Wang, Chengcheng Yang, Youliu Zhang and Wanzhen Zhang
Mathematics 2025, 13(17), 2899; https://doi.org/10.3390/math13172899 - 8 Sep 2025
Viewed by 543
Abstract
Accurate identification of weed species is a fundamental task for promoting efficient farmland management. Existing recognition approaches are typically based on either conventional Convolutional Neural Networks (CNNs) or the more recent Vision Transformers (ViTs). CNNs demonstrate strong capability in capturing local spatial patterns, [...] Read more.
Accurate identification of weed species is a fundamental task for promoting efficient farmland management. Existing recognition approaches are typically based on either conventional Convolutional Neural Networks (CNNs) or the more recent Vision Transformers (ViTs). CNNs demonstrate strong capability in capturing local spatial patterns, yet they are often limited in modeling long-range dependencies. In contrast, ViTs can effectively capture global contextual information through self-attention, but they may neglect fine-grained local features. These inherent shortcomings restrict the recognition performance of current models. To overcome these limitations, we propose a lightweight hybrid architecture, termed RepEfficientViT,which integrates convolutional operations with Transformer-based self-attention. This design enables the simultaneous aggregation of both local details and global dependencies. Furthermore, we employ a structural re-parameterization strategy to enhance the representational capacity of convolutional layers without introducing additional parameters or computational overhead. Experimental evaluations reveal that RepEfficientViT consistently surpasses state-of-the-art CNN and Transformer baselines. Specifically, the model achieves an accuracy of 94.77%, a precision of 94.75%, a recall of 94.93%, and an F1-score of 94.84%. In terms of efficiency, RepEfficientViT requires only 223.54 M FLOPs and 1.34 M parameters, while attaining an inference latency of merely 25.13 ms on CPU devices. These results demonstrate that the proposed model is well-suited for deployment in edge-computing scenarios subject to stringent computational and storage constraints. Full article
(This article belongs to the Special Issue Computational Intelligence, Computer Vision and Pattern Recognition)
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