Cited by 344Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image Fusion Techniques

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

Zhou, Wujie, Liu, Jinfu, Lei, Jingsheng, Yu, Lu, Hwang, Jenq-Neng·2021·IEEE Transactions on Image Processing

DOI 10.1109/tip.2021.3109518

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1109/tip.2021.3109518

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
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
Pyramid Scene Parsing Network
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
Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898
Deep High-Resolution Representation Learning for Human Pose Estimation
Sun, K., Xiao, B., Liu, D., & Wang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00584 · Cited by 5,984
Learning Deconvolution Network for Semantic Segmentation
Noh, H., Hong, S., & Han, B. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.178 · Cited by 4,422

Flagged first, then by citations

Show all 56 references

Sorted by citations

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
Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity
Tang, L., Zhang, H., Xu, H., & Ma, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101870 · Cited by 418
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
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
Dif-Fusion: Toward High Color Fidelity in Infrared and Visible Image Fusion With Diffusion Models
Yue, J., Fang, L., Xia, S., Deng, Y., & Ma, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3322046 · Cited by 304
WaveNet: Wavelet Network With Knowledge Distillation for RGB-T Salient Object Detection
Zhou, W., Sun, F., Jiang, Q., Cong, R., & Hwang, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3275538 · Cited by 231
Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption
Liu, J., Wu, G., Liu, Z., Wang, D., Jiang, Z., Ma, L., Zhong, W., Fan, X., & Liu, R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3521416 · Cited by 238
A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
Younesi, A., Ansari, M., Fazli, M., Ejlali, A., Shafique, M., & Henkel, J. (2024) · IEEE Access · doi:10.1109/access.2024.3376441 · Cited by 241
Delivering Arbitrary-Modal Semantic Segmentation
Zhang, J., Liu, R., Shi, H., Yang, K., Reiß, S., Peng, K., Fu, H., Wang, K., & Stiefelhagen, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00116 · Cited by 215
RGB-T Semantic Segmentation With Location, Activation, and Sharpening
Li, G., Wang, Y., Liu, Z., Zhang, X., & Zeng, D. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3208833 · Cited by 197
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
Liang, M., Hu, J., Bao, C., Hua, F., Deng, F., & Lam, T. L. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3272269 · Cited by 179
MTANet: Multitask-Aware Network With Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding
Zhou, W., Dong, S., Lei, J., & Yu, L. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3164899 · Cited by 183
PGDENet: Progressive Guided Fusion and Depth Enhancement Network for RGB-D Indoor Scene Parsing
Zhou, W., Yang, E., Lei, J., Wan, J., & Yu, L. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3161852 · Cited by 168
MMSMCNet: Modal Memory Sharing and Morphological Complementary Networks for RGB-T Urban Scene Semantic Segmentation
Zhou, W., Zhang, H., Yan, W., & Lin, W. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3275314 · Cited by 157
Edge-Aware Guidance Fusion Network for RGB–Thermal Scene Parsing
Zhou, W., Dong, S., Xu, C., & Qian, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20269 · Cited by 146
Remote-Sensing Scene Classification via Multistage Self-Guided Separation Network
Wang, J., Li, W., Zhang, M., Tao, R., & Chanussot, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3295797 · Cited by 139
Context-Aware Interaction Network for RGB-T Semantic Segmentation
Lv, Y., Liu, Z., & Li, G. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3349072 · Cited by 145
Thermal Infrared Image Colorization for Nighttime Driving Scenes With Top-Down Guided Attention
Luo, F., Li, Y., Zeng, G., Peng, P., Wang, G., & Li, Y. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3145476 · Cited by 115
FRNet: Feature Reconstruction Network for RGB-D Indoor Scene Parsing
Zhou, W., Yang, E., Lei, J., & Yu, L. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3174338 · Cited by 114
A Task-Guided, Implicitly-Searched and Meta-Initialized Deep Model for Image Fusion
Liu, R., Liu, Z., Liu, J., Fan, X., & Luo, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3382308 · Cited by 114
APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection
Zhou, W., Zhu, Y., Lei, J., Wan, J., & Yu, L. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3118043 · Cited by 110
SGFNet: Semantic-Guided Fusion Network for RGB-Thermal Semantic Segmentation
Wang, Y., Li, G., & Liu, Z. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3281419 · Cited by 104
Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
Wan, Z., Zhang, P., Wang, Y., Yong, S., Stepputtis, S., Sycara, K. P., & Xie, Y. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00176 · Cited by 114
Cross-Collaborative Fusion-Encoder Network for Robust RGB-Thermal Salient Object Detection
Liao, G., Gao, W., Li, G., Wang, J., & Kwong, S. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3184840 · Cited by 103
Mitigating Modality Discrepancies for RGB-T Semantic Segmentation
Zhao, S., Liu, Y., Jiao, Q., Zhang, Q., & Han, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3233089 · Cited by 102
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene Parsing
Dong, S., Zhou, W., Xu, C., & Yan, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3306368 · Cited by 111
DEFNet: Dual-Branch Enhanced Feature Fusion Network for RGB-T Crowd Counting
Zhou, W., Pan, Y. H., Lei, J., Ye, L., & Yu, L. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3203385 · Cited by 93
CIMFNet: Cross-Layer Interaction and Multiscale Fusion Network for Semantic Segmentation of High-Resolution Remote Sensing Images
Zhou, W., Jianhui, J., Lei, J., & Yu, L. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3159032 · Cited by 96
RGB-T image analysis technology and application: A survey
Song, K., Zhao, Y., Huang, L., Yan, Y., & Meng, Q. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105919 · Cited by 94
Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides
Jaafari, A., Panahi, M., Mafi-Gholami, D., Rahmati, O., Shahabi, H., Shirzadi, A., Lee, S., Bui, D. T., & Pradhan, B. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.108254 · Cited by 86
An extended MARCOS method for MCGDM under 2-tuple linguistic q-rung picture fuzzy environment
Akram, M., Khan, A., Luqman, A., Senapati, T., & Pamucar, D. (2023) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2023.105892 · Cited by 93
Greedy opposition-based learning for chimp optimization algorithm
Khishe, M. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10343-w · Cited by 76
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Ren, W., Tang, Y. S., Sun, Q., Zhao, C., & Han, Q. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123207 · Cited by 81
Glass Segmentation With RGB-Thermal Image Pairs
Huo, D., Wang, J., Qian, Y., & Yang, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3256762 · Cited by 73
Embedded Control Gate Fusion and Attention Residual Learning for RGB–Thermal Urban Scene Parsing
Zhou, W., Lv, Y. H., Lei, J., & Yu, L. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3242651 · Cited by 71
Vision-based food nutrition estimation via RGB-D fusion network
Shao, W., Min, W., Hou, S., Luo, M., Li, T., Zheng, Y., & Jiang, S. (2023) · Food Chemistry · doi:10.1016/j.foodchem.2023.136309 · Cited by 67
HFNet: Hierarchical feedback network with multilevel atrous spatial pyramid pooling for RGB-D saliency detection
Zhou, W., Liu, C., Lei, J., Yu, L., & Luo, T. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.100 · Cited by 64
Pixel Difference Convolutional Network for RGB-D Semantic Segmentation
Yang, J., Bai, L., Sun, Y., Tian, C., Mao, M., & Wang, G. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3296162 · Cited by 65
CGINet: Cross-modality grade interaction network for RGB-T crowd counting
Pan, Y. F., Zhou, W., Qian, X., Mao, S., Yang, R., & Yu, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106885 · Cited by 61
Part-Whole Relational Fusion Towards Multi-Modal Scene Understanding
Liu, Y., Li, C., Xu, S., & Han, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02393-8 · Cited by 59
Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58
Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images
Vinayaki, V. D., & Kalaiselvi, R. (2022) · Neural Processing Letters · doi:10.1007/s11063-021-10734-0 · Cited by 58
Dual-Space Graph-Based Interaction Network for RGB-Thermal Semantic Segmentation in Electric Power Scene
Xu, C. X., Li, Q., Jiang, X., Yu, D., & Zhou, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3216313 · Cited by 56
On the Role of Thermal Imaging in Automotive Applications: A Critical Review
Farooq, M. A., Shariff, W., O'Callaghan, D., Merla, A., & Corcoran, P. (2023) · IEEE Access · doi:10.1109/access.2023.3255110 · Cited by 58
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing
Zhou, W., Dong, S., Fang, M., & Yu, L. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3314527 · Cited by 57
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
Liu, Z., Liu, J., Zhang, B., Ma, L., Fan, X., & Liu, R. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611928 · Cited by 56
Instance segmentation ship detection based on improved Yolov7 using complex background SAR images
Yasir, M., Zhan, L., Liu, S., Wan, J., Hossain, M. S., Colak, A. T. I., Liu, M., Islam, Q. U., Mehdi, S. R., & Yang, Q. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1113669 · Cited by 55
DHFNet: dual-decoding hierarchical fusion network for RGB-thermal semantic segmentation
Cai, Y., Zhou, W., Zhang, L., Yu, L., & Luo, T. (2023) · The Visual Computer · doi:10.1007/s00371-023-02773-6 · Cited by 53
GCNet: Grid-like context-aware network for RGB-thermal semantic segmentation
Liu, J., Zhou, W., Cui, Y., Yu, L., & Luo, T. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.041 · Cited by 53
Semantic Segmentation for Thermal Images: A Comparative Survey
Kutuk, Z., & Algan, G. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00043 · Cited by 54
DRNet: Dual-stage refinement network with boundary inference for RGB-D semantic segmentation of indoor scenes
Yang, E., Zhou, W., Qian, X., Lei, J., & Yu, L. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106729 · Cited by 52
RETRACTED: Transformer-induced graph reasoning for multimodal semantic segmentation in remote sensing
He, Q., Sun, X., Diao, W., Yan, Z., Yin, D., & Fu, K. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.08.010 · Cited by 51
Retracted
CEGFNet: Common Extraction and Gate Fusion Network for Scene Parsing of Remote Sensing Images
Zhou, W., Jianhui, J., Lei, J., & Hwang, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3109626 · Cited by 48
Machine learning based IoT system for secure traffic management and accident detection in smart cities
Saravana Balaji, B., Balaji, P., Munshi, A. M., ALMUKADI, W. S., Prabhu, T. N., Venkatachalam, K., & Abouhawwash, M. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1259 · Cited by 57
Infrared and Visible Image Fusion via Multiscale Receptive Field Amplification Fusion Network
Ji, C., Zhou, W., Lei, J., & Ye, L. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3270759 · Cited by 46
MGCNet: Multilevel Gated Collaborative Network for RGB-D Semantic Segmentation of Indoor Scene
Yang, E., Zhou, W., Qian, X., & Yu, L. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3229594 · Cited by 45
CSANet: Contour and Semantic Feature Alignment Fusion Network for Rail Surface Defect Detection
Yang, J., Zhou, W., Wu, R., & Fang, M. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3299218 · Cited by 44
DCANet: Differential convolution attention network for RGB-D semantic segmentation
Bai, L., Yang, J., Tian, C., Sun, Y., Mao, M., Xu, Y., & Wei-rong, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111379 · Cited by 44
Swin Transformer Embedding Dual-Stream for Semantic Segmentation of Remote Sensing Imagery
Zhou, X., Zhou, L., Gong, S., Zhong, S., Yan, W. Q., & Huang, Y. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3326967 · Cited by 43
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Dong, S., Feng, Y., Yang, Q., Huang, Y., Liu, D., & Fan, H. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801872 · Cited by 46
A Novel Highway Routing Protocol in Vehicular Ad Hoc Networks Using VMaSC‐LTE and DBA‐MAC Protocols
Khezri, E., Zeinali, E., & Sargolzaey, H. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1680507 · Cited by 42
An Improved CycleGAN-Based Model for Low-Light Image Enhancement
Tang, G., Ni, J., Chen, Y., Cao, W., & Yang, S. X. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3296167 · Cited by 42
MMSFormer: Multimodal Transformer for Material and Semantic Segmentation
Reza, M. K., Prater-Bennette, A., & Asif, M. S. (2024) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2024.3389812 · Cited by 44
Global contextually guided lightweight network for RGB-thermal urban scene understanding
Gong, T., Zhou, W., Qian, X., Lei, J., & Yu, L. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105510 · Cited by 43
Multi-source collaborative enhanced for remote sensing images semantic segmentation
Zhao, J., Zhang, D., Shi, B., Zhou, Y. Z., Chen, J., Yao, R., & Xue, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.045 · Cited by 41
AGFNet: Adaptive Gated Fusion Network for RGB-T Semantic Segmentation
Zhou, X., Wu, X., Bao, L., Yin, H., Jiang, Q., & Zhang, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3528064 · Cited by 42
Depth Repeated-Enhancement RGB Network for Rail Surface Defect Inspection
Wu, J., Zhou, W., Qiu, W., & Yu, L. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3211199 · Cited by 40
Complementary Random Masking for RGB-Thermal Semantic Segmentation
Shin, U., Lee, K., Kweon, I. S., & Oh, J. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611200 · Cited by 49
“Therefore, it naturally led to recent active studies on semantic segmentation of RGB-thermal images [11], [36], [35], [37], [48], [17], [21], [47].”
BackgroundMethodInfluential
RCNet: Dual-Network Resonance Collaboration via Mutual Learning for RGB-D Road Defect Detection
Zhou, W., Ju, Z., Cong, R., & Yan, W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3617769 · Cited by 39
Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
Hu, H., Shan, W., Tang, Y., Heidari, A. A., Chen, H., Liu, H., Wang, M., Escorcia‐Gutierrez, J., Mansour, R. F., & Chen, J. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac119 · Cited by 35
Domain-invariant information aggregation for domain generalization semantic segmentation
Liao, M., Tian, S., Zhang, Y., Hua, G., Zou, W., & Li, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126273 · Cited by 35
THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images
Xu, G., Zhou, W., Qian, X., Zhang, Y., Lei, J., & Yu, L. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104011 · Cited by 34
MENet: Lightweight multimodality enhancement network for detecting salient objects in RGB-thermal images
Wu, J., Zhou, W., Qian, X., Lei, J., Yu, L., & Luo, T. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.024 · Cited by 33
Segmentation of Road Negative Obstacles Based on Dual Semantic-Feature Complementary Fusion for Autonomous Driving
Feng, Z., Guo, Y., & Sun, Y. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3376534 · Cited by 34
Learning multi-level structural information for small organ segmentation
Liu, Y., Duan, Y., & Zeng, T. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108418 · Cited by 32
Time series-dependent feature of EEG signals for improved visually evoked emotion classification using EmotionCapsNet
Kumari, N., Anwar, S., & Bhattacharjee, V. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-06942-x · Cited by 32
MFENet: Multitype fusion and enhancement network for detecting salient objects in RGB-T images
Wu, J., Zhou, W., Qian, X., Lei, J., Yu, L., & Luo, T. (2022) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103827 · Cited by 31
A RGB-Thermal based adaptive modality learning network for day–night wildfire identification
Xue, R., Li, Z., Zhang, X., Li, Z., & Song, W. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103554 · Cited by 31
Difference-Complementary Learning and Label Reassignment for Multimodal Semi-Supervised Semantic Segmentation of Remote Sensing Images
Han, W., Jiang, W. R., Geng, J., & Wang, M. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3526064 · Cited by 30
Integrating satellite and street-level images for local climate zone mapping
Cao, R., Liao, C., Li, Q., Tu, W., Zhu, R., Luo, N., Qiu, G., & Shi, W. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103323 · Cited by 29
Multi-Branch Differential Bidirectional Fusion Network for RGB-T Semantic Segmentation
Liang, W., Shan, C., Yang, Y., & Han, J. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3374793 · Cited by 30
SFINet: A semantic feature interactive learning network for full-time infrared and visible image fusion
Song, W., Li, Q., Gao, M., Chehri, A., & Jeon, G. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125472 · Cited by 28
CGFNet: cross-guided fusion network for RGB-thermal semantic segmentation
Fu, Y., Chen, Q., & Zhao, H. (2022) · The Visual Computer · doi:10.1007/s00371-022-02559-2 · Cited by 29
Remote Sensing Image Road Segmentation Method Integrating CNN-Transformer and UNet
Wang, R., Cai, M., Xia, Z., & Zhou, Z. (2023) · IEEE Access · doi:10.1109/access.2023.3344797 · Cited by 27
EISNet: A Multi-Modal Fusion Network for Semantic Segmentation With Events and Images
Xie, B., Deng, Y., Shao, Z., & Li, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3380255 · Cited by 30
Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies
Nagasree, Y., Rupa, C., Akshitha, P., Srivastava, G., Gadekallu, T. R., & Lakshmanna, K. (2023) · Drones · doi:10.3390/drones7010053 · Cited by 28
A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving
Li, G., Lin, Y., Ouyang, D., Li, S., Luo, X., Qu, X., Pi, D., & Li, S. E. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3332350 · Cited by 28
Blind Tone-Mapped HDR Image Quality Measurement by Analysis of Low-Level and High-Level Perceptual Characteristics
Cui, Y., Yu, M., Jiang, G., Peng, Z., & Chen, F. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3205928 · Cited by 26
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
Ji, W., Li, J., Bian, C., Zhou, Z., Zhao, J., Yuille, A., & Cheng, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00112 · Cited by 29
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
Liu, Q., Kampffmeyer, M., Jenssen, R., & Salberg, A.-B. (2022) · International Journal of Remote Sensing · doi:10.1080/01431161.2022.2098078 · Cited by 25
DHFNet: Decoupled Hierarchical Fusion Network for RGB-T dense prediction tasks
Chen, H., Wang, Z., Qin, H., & Mu, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127594 · Cited by 24
ECFNet: Efficient cross-layer fusion network for real time RGB-Thermal urban scene parsing
Shen, Z., Wang, J., Weng, Y., Pan, Z., Li, Y., & Wang, J. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104579 · Cited by 24
Local Visual and Global Deep Features Based Blind Stitched Panoramic Image Quality Evaluation Using Ensemble Learning
Cui, Y., Jiang, G., Yu, M., & Song, Y. (2022) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2022.3165935 · Cited by 24
Hierarchical Decoding Network Based on Swin Transformer for Detecting Salient Objects in RGB-T Images
Sun, F., Zhou, W., Ye, L., & Yu, L. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3194843 · Cited by 24
A Comparative Study of the Simulation Accuracy and Efficiency for the Urban Wind Environment Based on CFD Plug-Ins Integrated into Architectural Design Platforms
Hu, Y., Xu, F., & Gao, Z. J. (2022) · Buildings · doi:10.3390/buildings12091487 · Cited by 24
A deep thermal-guided approach for effective low-light visible image enhancement
Cao, Y., Tong, X., Wang, F., Yang, J., Cao, Y., Strat, S. T., & Tisse, C.-L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.12.007 · Cited by 24
DPNet: Dual-Pyramid Semantic Segmentation Network Based on Improved Deeplabv3 Plus
Wang, J., Zhang, X., Yan, T., & Tan, A. (2023) · Electronics · doi:10.3390/electronics12143161 · Cited by 24
Resolving semantic conflicts in RGB-T semantic segmentation
Zhao, S., Jin, Z., Jiao, Q., Zhang, Q., & Han, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111398 · Cited by 22
Performance optimization of annealing salp swarm algorithm: frameworks and applications for engineering design
Song, J., Chen, C., Heidari, A. A., Liu, J., Yu, H., & Chen, H. (2022) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwac021 · Cited by 23

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)
ORCID: Zhou, Wujie
Liu, Jinfu
Lei, Jingsheng
Yu, Lu
Hwang, Jenq-Neng
Venue: IEEE Transactions on Image Processing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021
Volume: 30
Pages: 7790-7802
ISSN: 1057-7149 (ISSN-L)
1941-0042
DOI: 10.1109/tip.2021.3109518
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks