The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation
Abstract
Semantic segmentation is a fundamental task in computer vision, and it has various applications in fields such as robotic sensing, video surveillance, and autonomous driving. A major research topic in urban road semantic segmentation is the proper integration and use of cross-modal information for fusion. Here, we attempt to leverage inherent multimodal information and acquire graded features to develop a novel multilabel-learning network for RGB-thermal urban scene semantic segmentation. Specifically, we propose a strategy for graded-feature extraction to split multilevel features into junior, intermediate, and senior levels. Then, we integrate RGB and thermal modalities with two distinct fusion modules, namely a shallow feature fusion module and deep feature fusion module for junior and senior features. Finally, we use multilabel supervision to optimize the network in terms of semantic, binary, and boundary characteristics. Experimental results confirm that the proposed architecture, the graded-feature multilabel-learning network, outperforms state-of-the-art methods for urban scene semantic segmentation, and it can be generalized to depth data.
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“| GMNet [112] | ResNet50 [16] | 57.3 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [112]
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Cites
Deep Residual Learning for Image Recognition
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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Densely Connected Convolutional Networks
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Fully convolutional networks for semantic segmentation
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Deep High-Resolution Representation Learning for Human Pose Estimation
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Learning Deconvolution Network for Semantic Segmentation
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RIME: A physics-based optimization
Su, H., Zhao, D., Heidari, A. A., Liu, L., Zhang, X., Mafarja, M. M., & Chen, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.010 · Cited by 1,135
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300537 · Cited by 731
Tang, L., Zhang, H., Xu, H., & Ma, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101870 · Cited by 418
Liu, J., Liu, Z., Wu, G., Ma, L., Liu, R., Zhong, W., Luo, Z., & Fan, X. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00745 · Cited by 346
LSNet: Lightweight Spatial Boosting Network for Detecting Salient Objects in RGB-Thermal Images
Zhou, W., Zhu, Y., Lei, J., Yang, R., & Yu, L. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3242775 · Cited by 304
Metadata
- Authors
- Zhou, W., Liu, J., Lei, J., Yu, L., & Hwang, J.-N.
- Affiliations
- Zhejiang University of Science and Technology (CN)
- Zhejiang University (CN)
- University of Washington (US)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2021
- Volume
- 30
- Pages
- 7790-7802
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W3198062544
- Cited by
- 344
- References
- 56
- Citations by year
- 2026: 43 · 2025: 90 · 2024: 57 · 2023: 76 · 2022: 70 · 2021: 8
- FWCI
- 18.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (61502429, 61972357)
- Zhejiang Provincial Natural Science Foundation of China (LY18F020012)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Keywords
- RGB-thermal fusion, thermal infrared imagery, multilabel learning, cross-modal fusion, shallow and deep feature fusion, boundary supervision, road scene understanding
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