Multimodal Learning and Transfer Learning

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 2257

Special Issue Editors

School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: zero-shot learning; domain adaptation; deep learning; machine learning; affective computing
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Guest Editor
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: transfer learning; zero-shot learning

E-Mail Website
Guest Editor
College of Computer Science, Sichuan University, Chengdu 610065, China
Interests: representation learning; unsupervised/semi-supervised learning; deep learning

Special Issue Information

Dear Colleagues,

Multimodal learning and transfer learning, as popular research directions in the field of artificial intelligence, are leading the way in the development of artificial intelligence technology. Multimodal learning involves joint modeling and learning using data in different modalities (e.g., text, image, speech, etc.) to gain deeper understanding and reasoning capabilities. Transfer learning, on the other hand, explores how to transfer learned knowledge or experience to new tasks or domains to speed up the learning process and improve performance. This Special Issue brings together the latest research results and trends in multimodal learning and transfer learning and aims to provide a platform for academics and the industry to discuss these two fields in depth, to promote communication and cooperation in the fields of multimodal learning and transfer learning, and to facilitate the implementation of related technologies and innovations in practical applications.

We invite researchers from academia and industry to contribute their original research articles, reviews, and case studies. Topics of study can include, but are not limited to, the following:

  1. Multimodal learning models and algorithms;
  2. Cross-modal information fusion and representation learning;
  3. Theory and methods of migratory learning;
  4. Cross-domain transfer learning;
  5. Cross-language multimodal learning;
  6. Applications of multimodal learning and transfer learning (applied to natural language processing, image processing, audio processing and speech recognition, intelligent recommendation systems, etc.).

Dr. Yalan Ye
Dr. Jingjing Li
Dr. Shudong Huang
Guest Editors

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Keywords

  • multimodal learning
  • transfer learning
  • deep learning
  • machine learning
  • domain adaptation
  • artificial intelligence

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Published Papers (1 paper)

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Research

17 pages, 929 KiB  
Article
Vehicle Simulation Algorithm for Observations with Variable Dimensions Based on Deep Reinforcement Learning
by Yunzhuo Liu, Ruoning Zhang and Shijie Zhou
Electronics 2023, 12(24), 5029; https://doi.org/10.3390/electronics12245029 - 16 Dec 2023
Viewed by 1608
Abstract
Vehicle simulation algorithms play a crucial role in enhancing traffic efficiency and safety by predicting and evaluating vehicle behavior in various traffic scenarios. Recently, vehicle simulation algorithms based on reinforcement learning have demonstrated excellent performance in practical tasks due to their ability to [...] Read more.
Vehicle simulation algorithms play a crucial role in enhancing traffic efficiency and safety by predicting and evaluating vehicle behavior in various traffic scenarios. Recently, vehicle simulation algorithms based on reinforcement learning have demonstrated excellent performance in practical tasks due to their ability to exhibit superior performance with zero-shot learning. However, these algorithms face challenges in field adaptation problems when deployed in task sets with variable-dimensional observations, primarily due to the inherent limitations of neural network models. In this paper, we propose a neural network structure accommodating variations in specific dimensions to enhance existing reinforcement learning methods. Building upon this, a scene-compatible vehicle simulation algorithm is designed. We conducted experiments on multiple tasks and scenarios using the Highway-Env traffic environment simulator. The results of our experiments demonstrate that the algorithm can successfully operate on all tasks using a neural network model with fixed shape, even with variable-dimensional observations. Our model exhibits no degradation in simulation performance when compared to the baseline algorithm. Full article
(This article belongs to the Special Issue Multimodal Learning and Transfer Learning)
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