Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)

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Research of Improved DETR Models and Transformer Applications in Computer Vision

Authors
Ruoyu Li1, *
1Petroleum Institute, China University of Petroleum, Beijing, 834000, China
*Corresponding author. Email: 2022015511@st.cupk.edu.cn
Corresponding Author
Ruoyu Li
Available Online 16 October 2024.
DOI
10.2991/978-94-6463-540-9_98How to use a DOI?
Keywords
DETR Models; Transformer; Computer Vision
Abstract

Researchers in the domain of computer vision have increasingly turned their attention towards harnessing the power of Transformer models for visual tasks. This paradigm shift has led to the emergence of pioneering models such as Detection Transformer (DETR) and Vision Transformer (ViT), opening up new frontiers for advancement in computer vision research. In this article, the significance of this transition and its implications for target detection are explored. Specifically, light is shed on the inherent limitations of the DETR model in effectively identifying targets in visual data, paving the way for a comprehensive discussion on strategies for enhancing its performance. Through an exploration of various DETR models and their innovative approaches, readers are provided with a nuanced understanding of the challenges and opportunities in target detection within the context of Transformer-based methodologies. By elucidating the guiding principles driving the evolution of DETR models, valuable insights into the future trajectory of computer vision research and the transformative potential of Transformer technology in visual perception tasks are offered.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
Series
Advances in Computer Science Research
Publication Date
16 October 2024
ISBN
978-94-6463-540-9
ISSN
2352-538X
DOI
10.2991/978-94-6463-540-9_98How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ruoyu Li
PY  - 2024
DA  - 2024/10/16
TI  - Research of Improved DETR Models and Transformer Applications in Computer Vision
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 981
EP  - 992
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_98
DO  - 10.2991/978-94-6463-540-9_98
ID  - Li2024
ER  -