An overview of research results and applications in the field of semi-supervised semantic segmentation

Research Article
Open access

An overview of research results and applications in the field of semi-supervised semantic segmentation

Dingrui Shi 1*
  • 1 Zhejiang University of Finance & Economics    
  • *corresponding author 19157706302@163.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/21/20231126
ACE Vol.21
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-033-2
ISBN (Online): 978-1-83558-034-9

Abstract

Traditional segmentation approaches, supervised deep learning methods, and semi-supervised deep learning methods have all found widespread use as the field of semi-supervised semantic segmentation has advanced. These methods have developed and progressed over time, opening up novel avenues of research in the field of image segmentation and giving potent resources for tackling difficult practical issues. These developments have deepened our understanding of image segmentation and provided flexible and efficient solutions to challenges in practical applications, ranging from classical traditional approaches to supervised methods based on deep learning, and beyond to semi-supervised methods that leverage both labeled and unlabeled data. Focusing on their specialized applications in medical and remote sensing image processing, this paper presents a complete overview of the development status of these methods. This study's image segmentation solutions can help tackle actual-world issues where annotated data is rare or expensive to some extent.

Keywords:

semi-supervised semantic segmentation, Generative Adversarial Networks, the pseudo-labeling method, consistency regularization

Shi,D. (2023). An overview of research results and applications in the field of semi-supervised semantic segmentation. Applied and Computational Engineering,21,108-113.
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References

[1]. Pel'aez-Vegas, A., Mesejo, P., & Luengo, J. (2023). A Survey on Semi-Supervised Semantic Segmentation. ArXiv, abs/2302.09899.

[2]. Long, Y., Zhang, Q., Zeng, B., Gao, L., Liu, X., Zhang, J., & Song, J. (2022). Frequency Domain Model Augmentation for Adversarial Attack. ArXiv, abs/2207.05382.

[3]. Wang, H., Chen, T., Gui, S., Hu, T., Liu, J., & Wang, Z. (2020). Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. ArXiv, abs/2010.11828.

[4]. Wang, Y., Wang, H., Shen, Y., Fei, J., Li, W., Jin, G., Wu, L., Zhao, R., & Le, X. (2022). Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4238-4247.

[5]. Tan, C., Gao, Z., Wu, L., Li, S., & Li, S. (2022). Hyperspherical Consistency Regularization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7234-7245.

[6]. Kim, J., Min, Y., Kim, D., Lee, G., Seo, J., Ryoo, K., & Kim, S. (2022). ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization. ArXiv, abs/2208.08631.

[7]. Hu, X., Zeng, D., Xu, X., & Shi, Y. (2021). Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention.

[8]. Hu, X., Zeng, D., Xu, X., & Shi, Y. (2021). Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention.

[9]. LIU Yu-Xi,ZHANG Bo,WANG Bin.Semi-supervised semantic segmentation based on Generative Adversarial Networks for remote sensing images[J].Journal of INFRARED AND MILLIMETER WAVES,2020,39(04):473-482.

Cite this article

Shi,D. (2023). An overview of research results and applications in the field of semi-supervised semantic segmentation. Applied and Computational Engineering,21,108-113.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-033-2(Print) / 978-1-83558-034-9(Online)
Editor：Roman Bauer, Alan Wang, Marwan Omar
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.21
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Pel'aez-Vegas, A., Mesejo, P., & Luengo, J. (2023). A Survey on Semi-Supervised Semantic Segmentation. ArXiv, abs/2302.09899.

[2]. Long, Y., Zhang, Q., Zeng, B., Gao, L., Liu, X., Zhang, J., & Song, J. (2022). Frequency Domain Model Augmentation for Adversarial Attack. ArXiv, abs/2207.05382.

[3]. Wang, H., Chen, T., Gui, S., Hu, T., Liu, J., & Wang, Z. (2020). Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. ArXiv, abs/2010.11828.

[4]. Wang, Y., Wang, H., Shen, Y., Fei, J., Li, W., Jin, G., Wu, L., Zhao, R., & Le, X. (2022). Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4238-4247.

[5]. Tan, C., Gao, Z., Wu, L., Li, S., & Li, S. (2022). Hyperspherical Consistency Regularization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7234-7245.

[6]. Kim, J., Min, Y., Kim, D., Lee, G., Seo, J., Ryoo, K., & Kim, S. (2022). ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization. ArXiv, abs/2208.08631.

[7]. Hu, X., Zeng, D., Xu, X., & Shi, Y. (2021). Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention.

[8]. Hu, X., Zeng, D., Xu, X., & Shi, Y. (2021). Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention.

[9]. LIU Yu-Xi,ZHANG Bo,WANG Bin.Semi-supervised semantic segmentation based on Generative Adversarial Networks for remote sensing images[J].Journal of INFRARED AND MILLIMETER WAVES,2020,39(04):473-482.

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