Advances of Artificial Intelligence and Vision Applications, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 25 February 2025 | Viewed by 1417

Special Issue Editors

Special Issue Information

Dear Colleagues,

Artificial intelligence technologies represented by deep learning and convolutional neural networks have greatly promoted the research and development of computer vision in the last decade. Simultaneously, advances in software and hardware also enable engineers to implement their elaborated computer vision algorithms onto powerful platforms. These advancements have enabled computer vision to attain enormous success across every aspect of modern society, including agriculture, retail, insurance, manufacturing, logistics, smart city, healthcare, pharmaceutical, construction, etc. The performance of an AI-based computer vision system is still constrained by the quality and quantity of training data and the hardware platforms' computing power and processing speed. This Special Issue aims to collect the advances and contributions of related research to the design, optimization, and implementation of artificial intelligence and computer vision applications.

General topics covered in this Special Issue include, but are not limited to, the following:

  • Image interpretation;
  • Object recognition and tracking;
  • Shape analysis, monitoring, and surveillance;
  • Biologically inspired computer vision;
  • Motion analysis;
  • Document image understanding;
  • Face and gesture recognition;
  • Vision-based human–computer interaction;
  • Human activity and behavior understanding;
  • Emotion recognition.

Dr. Dong Zhang
Prof. Dr. Dah-Jye Lee
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 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. Electronics 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 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

  • artificial intelligence
  • computer vision
  • deep learning
  • convolutional neural networks
  • affective computing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

23 pages, 2687 KiB  
Article
A New Joint Training Method for Facial Expression Recognition with Inconsistently Annotated and Imbalanced Data
by Tao Chen, Dong Zhang and Dah-Jye Lee
Electronics 2024, 13(19), 3891; https://doi.org/10.3390/electronics13193891 - 1 Oct 2024
Viewed by 923
Abstract
Facial expression recognition (FER) plays a crucial role in various applications, including human–computer interaction and affective computing. However, the joint training of an FER network with multiple datasets is a promising strategy to enhance its performance. Nevertheless, widespread annotation inconsistencies and class imbalances [...] Read more.
Facial expression recognition (FER) plays a crucial role in various applications, including human–computer interaction and affective computing. However, the joint training of an FER network with multiple datasets is a promising strategy to enhance its performance. Nevertheless, widespread annotation inconsistencies and class imbalances among FER datasets pose significant challenges to this approach. This paper proposes a new multi-dataset joint training method, Sample Selection and Paired Augmentation Joint Training (SSPA-JT), to address these challenges. SSPA-JT models annotation inconsistency as a label noise problem and selects clean samples from auxiliary datasets to expand the overall dataset size while maintaining consistent annotation standards. Additionally, a dynamic matching algorithm is developed to pair clean samples of the tail class with noisy samples, which enriches the tail classes with diverse background information. Experimental results demonstrate that SSPA-JT achieved superior or comparable performance compared with the existing methods by addressing both annotation inconsistencies and class imbalance during multi-dataset joint training. It achieved state-of-the-art performance on RAF-DB and CAER-S datasets with accuracies of 92.44% and 98.22%, respectively, reflecting improvements of 0.2% and 3.65% over existing methods. Full article
Show Figures

Figure 1

Back to TopTop