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Future Internet
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Advances in Deep Learning and Next-Generation Internet Technologies
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Advances in Deep Learning and Next-Generation Internet Technologies

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 897

Special Issue Editors

Dr. Shun Zhang

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Interests: intelligent image processing; machine learning and artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Gao

E-Mail Website
Guest Editor
Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: hyperspectral image processing; artificial intelligence in oceanography
Special Issues, Collections and Topics in MDPI journals
Dr. Mingyang Ma

E-Mail
Guest Editor
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: remote sensing image processing; video understanding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the research on deep learning algorithms and their applications within next-generation Internet technologies. Deep-learning-based approaches have demonstrated significant success in areas such as computer vision, natural language processing, and autonomous systems. However, as Internet technologies evolve with the rise of 5G/6G, edge computing, IoT, and cloud infrastructures, the integration of these two fields offers new challenges and opportunities for more efficient, scalable, and real-time AI solutions.

This Special Issue aims to furnish a thorough exploration of recent advancements and emerging trends in the domain of deep learning, including the development of novel architecture design, attention mechanisms for feature extraction, training methodologies, model compression, and various specific applications. Moreover, the Special Issue also explores how deep learning algorithms can be optimized, deployed, and enhanced when integrated with emerging Internet systems. By combining the power of AI with the infrastructure of connected systems, this Issue seeks to investigate the new methodologies, architectures, and applications that emerge from this fusion.

Topic areas include, but are not limited to, the following:

  • Novel deep learning algorithms;
  • Few-shot, zero-shot, and unsupervised learning;
  • Transfer learning and domain adaptation;
  • Attention mechanisms for feature extraction;
  • Model compression and optimization for deployment in edge/IoT devices;
  • Deep learning on edge computing and IoT, 5G/6G networks, and cloud computing;
  • AI in autonomous driving, smart cities, healthcare, and industrial IoT;
  • Artificial Intelligence of Things;
  • Remote sensing and satellite imaging using AI.

You may choose our Joint Special Issue in Algorithms.

Dr. Shun Zhang
Dr. Feng Gao
Dr. Mingyang Ma
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. Future Internet 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 1600 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

  • deep learning
  • model compression
  • feature extraction
  • edge computing
  • 5G/6G networks
  • cloud computing
  • autonomous driving
  • smart cities
  • healthcare
  • industrial IoT
  • remote sensing

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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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

24 pages, 2703 KiB  
Article
Unsupervised Person Re-Identification via Deep Attribute Learning
by Shun Zhang, Yaohui Xu, Xuebin Zhang, Boyang Cheng and Ke Wang
Future Internet 2025, 17(8), 371; https://doi.org/10.3390/fi17080371 - 15 Aug 2025
Abstract
Driven by growing public security demands and the advancement of intelligent surveillance systems, person re-identification (ReID) has emerged as a prominent research focus in the field of computer vision. %The primary objective of person ReID is to retrieve individuals with the same identity [...] Read more.
Driven by growing public security demands and the advancement of intelligent surveillance systems, person re-identification (ReID) has emerged as a prominent research focus in the field of computer vision. %The primary objective of person ReID is to retrieve individuals with the same identity across different camera views. However, this task presents challenges due to its high sensitivity to variations in visual appearance caused by factors such as body pose and camera parameters. Although deep learning-based methods have achieved marked progress in ReID, the high cost of annotation remains a challenge that cannot be overlooked. To address this, we propose an unsupervised attribute learning framework that eliminates the need for costly manual annotations while maintaining high accuracy. The framework learns the mid-level human attributes (such as clothing type and gender) that are robust to substantial visual appearance variations and can hence boost the accuracy of attributes with a small amount of labeled data. To carry out our framework, we present a part-based convolutional neural network (CNN) architecture, which consists of two components for image and body attribute learning on a global level and upper- and lower-body image and attribute learning at a local level. The proposed architecture is trained to learn attribute-semantic and identity-discriminative feature representations simultaneously. For model learning, we first train our part-based network using a supervised approach on a labeled attribute dataset. Then, we apply an unsupervised clustering method to assign pseudo-labels to unlabeled images in a target dataset using our trained network. To improve feature compatibility, we introduce an attribute consistency scheme for unsupervised domain adaptation on this unlabeled target data. During training on the target dataset, we alternately perform three steps: extracting features with the updated model, assigning pseudo-labels to unlabeled images, and fine-tuning the model. % change Through a unified framework that fuses complementary attribute-label and identity label information, our approach achieves considerable improvements of 10.6\% and 3.91\% mAP on Market-1501→DukeMTMC-ReID and DukeMTMC-ReID→Market-1501 unsupervised domain adaptation tasks, respectively. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)
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