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Research on Deep Neural Networks for Video Motion Recognition
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School of Computer Science and Technology, Zhoukou Normal University, Zhoukou 466001, China
Prof. Dr. Hong Su
School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
School of Automobile Engineering, Guilin University of Aerospace Technology, Guilin 541004, China
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Research on Deep Neural Networks for Video Motion Recognition

Abstract submission deadline
30 November 2024
Manuscript submission deadline
31 January 2025
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Topic Information

Dear Colleagues,

Deep neural networks have been widely used for video motion recognition tasks, such as action recognition, activity recognition, and gesture recognition. This Topic call aims to bridge the gap between theoretical research and practical applications in the field of video motion recognition using deep neural networks. The articles are expected to provide insights into the latest trends and advancements in the field and their potential to address real-world problems in various domains. The issue will also highlight the limitations and open research problems in the area, paving the way for future research directions. The contributions are expected to provide a comprehensive and detailed understanding of the underlying principles and techniques of deep-neural-network-based video motion recognition, facilitating the development of innovative solutions and techniques to overcome the existing challenges in the field. Topics of interest include but are not limited to:

  • Novel deep neural network architectures for video motion recognition;
  • Learning spatiotemporal features for video motion recognition;
  • Transfer learning and domain adaptation for video motion recognition;
  • Large-scale video datasets and benchmarking for video motion recognition;
  • Applications of deep neural networks for video motion recognition, such as human– computer interaction, surveillance, and sports analysis;
  • Applications of explainable artificial intelligence for video motion recognition.

Prof. Dr. Hamad Naeem
Prof. Dr. Hong Su
Prof. Dr. Amjad Alsirhani
Prof. Dr. Muhammad Shoaib Bhutta
Topic Editors

Keywords

  • deep learning
  • video analysis
  • motion detection
  • computer vision
  • neural networks

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Future Internet
futureinternet 		2.8 7.1 2009 13.1 Days CHF 1600 Submit
Information
information 		2.4 6.9 2010 14.9 Days CHF 1600 Submit
Journal of Imaging
jimaging 		2.7 5.9 2015 20.9 Days CHF 1800 Submit
Mathematics
mathematics 		2.3 4.0 2013 17.1 Days CHF 2600 Submit
Symmetry
symmetry 		2.2 5.4 2009 16.8 Days CHF 2400 Submit

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Published Papers (1 paper)

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Article
Temporal–Semantic Aligning and Reasoning Transformer for Audio-Visual Zero-Shot Learning
by Kaiwen Zhang, Kunchen Zhao and Yunong Tian
Mathematics 2024, 12(14), 2200; https://doi.org/10.3390/math12142200 - 13 Jul 2024
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Abstract
Zero-shot learning (ZSL) enables models to recognize categories not encountered during training, which is crucial for categories with limited data. Existing methods overlook efficient temporal modeling in multimodal data. This paper proposes a Temporal–Semantic Aligning and Reasoning Transformer (TSART) for spatio-temporal modeling. TSART [...] Read more.
Zero-shot learning (ZSL) enables models to recognize categories not encountered during training, which is crucial for categories with limited data. Existing methods overlook efficient temporal modeling in multimodal data. This paper proposes a Temporal–Semantic Aligning and Reasoning Transformer (TSART) for spatio-temporal modeling. TSART uses the pre-trained SeLaVi network to extract audio and visual features and explores the semantic information of these modalities through audio and visual encoders. It incorporates a temporal information reasoning module to enhance the capture of temporal features in audio, and a cross-modal reasoning module to effectively integrate audio and visual information, establishing a robust joint embedding representation. Our experimental results validate the effectiveness of this approach, demonstrating outstanding Generalized Zero-Shot Learning (GZSL) performance on the UCF101 Generalized Zero-Shot Learning (UCF-GZSL), VGGSound-GZSL, and ActivityNet-GZSL datasets, with notable improvements in the Harmonic Mean (HM) evaluation. These results indicate that TSART has great potential in handling complex spatio-temporal information and multimodal fusion. Full article
(This article belongs to the Topic Research on Deep Neural Networks for Video Motion Recognition)
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