FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation
Open accessAbstract
The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card.
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FEANet, using a ResNet101 backbone, achieves an mIoU of 55.3 on the MSSSD/MF dataset for RGB + Thermal semantic segmentation.
“| FEANet [113] | ResNet101 [16] | 55.3 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [113]
Cites
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 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
CBAM: Convolutional Block Attention Module
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853 · see citing works
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054 · see citing works
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 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522 · see citing works
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931 · see citing works
BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01261-8_20 · Cited by 2,968 · see citing works
SUN RGB-D: A RGB-D scene understanding benchmark suite
Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943 · see citing works
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1606.02147 · Cited by 1,826 · see citing works
ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation
Romera, E., Álvarez, J. M., Bergasa, L. M., & Arroyo, R. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2750080 · Cited by 1,491 · see citing works
A Review on Deep Learning Techniques Applied to Semantic SegmentationPreprint
García-García, A., Orts‐Escolano, S., Oprea, S., Villena-Martínez, V., & García‐Rodríguez, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.06857 · Cited by 1,049 · see citing works
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902 · see citing works
MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes
Ha, Q., Watanabe, K., Karasawa, T., Ushiku, Y., & Harada, T. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206396 · Cited by 693 · see citing works
Rtfnet: Rgb-thermal fusion network for semantic segmentation of urban scenes
Sun, Y., Zuo, W., & Liu, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2904733 · Cited by 546 · see citing works
Probabilistic End-To-End Noise Correction for Learning With Noisy Labels
Yi, K., & Wu, J. (2019) · 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/cvpr.2019.00718 · Cited by 481 · see citing works
Depth-Aware CNN for RGB-D Segmentation
Wang, W., & Neumann, U. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01252-6_9 · Cited by 314 · see citing works
FuseSeg: Semantic segmentation of urban scenes based on RGB and thermal data fusion
Sun, Y., Zuo, W., Yun, P., Wang, H., & Liu, M. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2020.2993143 · Cited by 270 · see citing works
Self-citationPST900: RGB-Thermal Calibration, Dataset and Segmentation Network
Shivakumar, S. S., Rodrigues, N., Zhou, A., Miller, I. D., Kumar, V., & Taylor, C. J. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196831 · Cited by 269 · see citing works
Importance-Aware Semantic Segmentation for Autonomous Vehicles
Chen, B., Gong, C., & Yang, J. J. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2018.2801309 · Cited by 121 · see citing works
Efficient Multi-cue Scene Segmentation
Scharwächter, T., Enzweiler, M., Franke, U., & Roth, S. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40602-7_46 · Cited by 87 · see citing works
Traffic Scene Segmentation Based on RGB-D Image and Deep Learning
Li, L., Qian, B., Lian, J., Zheng, W., & Zhou, Y. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2724138 · Cited by 77 · see citing works
Probabilistic End-to-end Noise Correction for Learning with Noisy LabelsPreprint
Yi, K., & Wu, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.07788 · Cited by 65 · see citing works
Multiband Image Segmentation and Object Recognition for Understanding Road Scenes
Kang, Y., Yamaguchi, K., Naito, T., & Ninomiya, Y. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2160539 · Cited by 52 · see citing works
Active Perception for Outdoor Localisation with an Omnidirectional Camera
Jayasuriya, M., Ranasinghe, R., & Dissanayake, G. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340974 · Cited by 25 · see citing works
Depth-aware CNN for RGB-D SegmentationPreprint
Wang, W., & Neumann, U. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06791 · Cited by 15 · see citing works
DeepFuseNet of Omnidirectional Far-Infrared and Visual Stream for Vegetation Detection
Stone, D. L., Ravi, S. S., Benli, E., & Motai, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3044487 · Cited by 4 · see citing works
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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 · see citing works
Tang, L., Zhang, H., Xu, H., & Ma, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101870 · Cited by 396 · see citing works
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 · see citing works
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 202 · see citing works
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 196 · see citing works
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 · see citing works
Self-citationZhou, 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 151 · see citing works
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 138 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
MRFS: Mutually Reinforcing Image Fusion and Segmentation
Zhang, H., Zuo, X., Jiang, J., Guo, C., & Ma, J. (2024) · 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/cvpr52733.2024.02547 · Cited by 98 · see citing works
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 · see citing works
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Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
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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 · see citing works
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“Specifically, they proposed various types of feature fusion modules, such as the na ¨ ıve feature-level fusion [11], [36], [35], multi-scale feature fusion [36], [46], [37], [21], and attention-weighted fusion [44], [46], [8], [21].”
BackgroundMethodCAGNet: Coordinated attention guidance network for RGB-T crowd counting
Yang, X., Zhou, W., Yan, W., & Qian, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122753 · Cited by 39 · see citing works
Shojaiee, F., & Baleghi, Y. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105627 · Cited by 34 · see citing works
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Zhang, J., Zhang, R., Yuan, W., & Liu, Y. (2024) · Evolving Systems · doi:10.1007/s12530-024-09567-8 · Cited by 21 · see citing works
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Feng, Y., Luo, E., Lu, H., & Zhai, S. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1356248 · Cited by 11 · see citing works
Semantic Guidance Fusion Network for Cross-Modal Semantic Segmentation
Zhang, P., Chen, M., & Gao, M. (2024) · Sensors · doi:10.3390/s24082473 · Cited by 10 · see citing works
HAFFseg: RGB-Thermal semantic segmentation network with hybrid adaptive feature fusion strategy
Yi, S., Chen, M., Liu, X., Li, J., & Chen, L. (2023) · Signal Processing Image Communication · doi:10.1016/j.image.2023.117027 · Cited by 10 · see citing works
TAG-fusion: Two-stage attention guided multi-modal fusion network for semantic segmentation
Zhang, Z., Wang, W., Zhu, L., & Tang, Z. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104807 · Cited by 9 · see citing works
PSNet: A Universal Algorithm for Multispectral Remote Sensing Image Segmentation
Zheng, Y., Chen, Z., Zheng, T., Tian, C., & Dong, W. (2025) · Remote Sensing · doi:10.3390/rs17040563 · Cited by 11 · see citing works
Domain Adaptation for Vehicle Detection Under Adverse Weather
Lin, H., Huang, Y., Lai, J., & You, T.-T. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2025.3563373 · Cited by 9 · see citing works
Distilling Hierarchical Knowledge From Multimodal Fusion for Unimodal Image Segmentation
Sun, Y., Dong, W., Wang, S., Wu, P., Feng, M., Li, X., & Shi, G. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3579580 · Cited by 9 · see citing works
Liao, C., Lei, K., Xu, Z., Moon, J., Wang, Z., Wang, Y., Paudel, D. P., Gool, L. V., & Hu, X. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00146 · Cited by 9 · see citing works
Saliency detection network with two-stream encoder and interactive decoder
Yang, A., Cheng, S., Song, S., Wang, J., Ji, Z., Pang, Y., & Cao, J. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.08.051 · Cited by 9 · see citing works
Illumination-aware window transformer for RGBT modality fusion
Zhou, L., & Chen, Z. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103725 · Cited by 9 · see citing works
A Review On Thermal Infrared Semantic Distribution for Nightfall Drive
Bandi, M., & Reeja, S. R. (2022) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict54557.2022.9917651 · Cited by 8 · see citing works
Yi, S., Wu, L., Liu, X., Li, J., & Jiang, G. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.105077 · Cited by 8 · see citing works
Semantic Segmentation for Autonomous Driving
Yang, J., Guo, S., Bocus, M. J., Chen, Q., & Fan, R. (2023) · Advances in computer vision and pattern recognition · doi:10.1007/978-981-99-4287-9_4 · Cited by 8 · see citing works
Zhou, X., Ma, Y., Huang, J., Cai, Z., Zhu, Y., & Fan, F. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3549516 · Cited by 7 · see citing works
Weakly supervised salient object detection via bounding-box annotation and SAM model
Liu, X., & Huang, X. (2024) · Electronic Research Archive · doi:10.3934/era.2024074 · Cited by 7 · see citing works
Thermal infrared image semantic segmentation for night-time driving scenes based on deep learning
Maheswari, B., & Reeja, S. R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15882-0 · Cited by 7 · see citing works
AMFFNet: Asymmetric Multiscale Feature Fusion Network of RGB-NIR for Solid Waste Detection
Cai, Z., Fang, H., Jiang, F., Yang, J., Ji, T., Hu, Y., & Wang, X. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3300445 · Cited by 7 · see citing works
A Lightweight RGB-T Fusion Network for Practical Semantic Segmentation
Zhang, H., Li, Z., Wu, Z., & Wang, D. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10421911 · Cited by 7 · see citing works
Hou, Y.-L., Jia, Y., Hou, Z., Hao, X., & Shen, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3227771 · Cited by 7 · see citing works
Du, R., Zhang, H., Huang, Z., Zhong, H., Cao, Y., & Wang, Y. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3459014 · Cited by 6 · see citing works
Hu, Y., Hu, Q., & Li, J. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3522942 · Cited by 6 · see citing works
Semantic Region Adaptive Fusion of Infrared and Visible Images via Dual-DeepLab Guidance
Cao, W., Zheng, M. M., & Liao, Q. M. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3318709 · Cited by 6 · see citing works
Simple Scalable Multimodal Semantic Segmentation Model
Zhu, Y., & Xiao, N. (2024) · Sensors · doi:10.3390/s24020699 · Cited by 5 · see citing works
NTFNet: Narrowing-Then-Fusing network for RGB-TIR semantic segmentation
Liu, Y., Ye, J., He, W., Qu, Z., & Xu, R. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06411-7 · Cited by 5 · see citing works
MGFNet: a multiscale gated fusion network for multimodal semantic segmentation
Wu, H., Li, Z., & Wen, Z. (2025) · The Visual Computer · doi:10.1007/s00371-025-03912-x · Cited by 6 · see citing works
Wang, Z., He, D., Zhao, L., Liu, B., Zheng, Y., & Zhang, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114481 · Cited by 6 · see citing works
Yang, F., Shao, F., Mu, B., Chai, X., & Jiang, Q. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3624821 · Cited by 5 · see citing works
Zhao, J., Teng, F., Luo, K., Zhao, G., Li, Z., Xu, Z., & Yang, K. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247337 · Cited by 5 · see citing works
Shen, Z., Pan, Z., Weng, Y., Li, Y., Wang, J., & Wang, J. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05743-0 · Cited by 5 · see citing works
Unveiling the Power of Visible-Thermal Video Object Segmentation
Yang, J., Gao, M., Cong, R., Wang, C., Zheng, F., & Leonardis, A. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3345852 · Cited by 5 · see citing works
Zhang, Y., Yu, P., Liu, T., & Wang, S. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.5.053049 · Cited by 5 · see citing works
Open-Vocabulary RGB-Thermal Semantic Segmentation
Zhao, G., Huang, J., Yan, X., Wang, Z., Tang, J., Ou, Y., Hu, X., & Peng, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72904-1_18 · Cited by 6 · see citing works
FCKDNet: A Feature Condensation Knowledge Distillation Network for Semantic Segmentation
Yuan, W., Lu, X., Zhang, R., & Liu, Y. (2023) · Entropy · doi:10.3390/e25010125 · Cited by 5 · see citing works
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
Liu, Y., Zhang, W., Zhao, G., Zhu, J., Vasilakos, A. V., & Wang, L. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3336611 · Cited by 5 · see citing works
Yue, T., Huang, H., Wang, Q., Song, B., & Chen, Y. (2025) · Applied Sciences · doi:10.3390/app151810268 · Cited by 4 · see citing works
Multimodal Segmentation for Photovoltaic Module Defect Detection
He, X., Tan, J., Hu, T., & Zhu, L. (2025) · IEEE Access · doi:10.1109/access.2025.3640579 · Cited by 4 · see citing works
Marinaccio, M., & Afghah, F. (2025) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf67917.2025.11443804 · Cited by 4 · see citing works
M-SpecGene: Generalized Foundation Model for RGBT Multispectral Vision
Zhou, K., Yang, F., Wang, S., Wen, B., Zi, C., Chen, L., Shen, Q., & Cao, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00737 · Cited by 4 · see citing works
- Authors
- Deng, F., Feng, H., Liang, M., Wang, H., Yang, Y., Gao, Y., Chen, J., Hu, J., Guo, X., & Lam, T. L.
- Affiliations
- Chinese University of Hong Kong, Shenzhen (CN)
- Wuyi University (CN)
- Venue
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2021-09-27
- Pages
- 4467-4473
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W3206439726
- Cited by
- 170
- References
- 31
- Citations by year
- 2026: 24 · 2025: 56 · 2024: 40 · 2023: 32 · 2022: 18
- FWCI
- 7.36
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Wuyi University
- Shenzhen Peacock Plan
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- thermal infrared imaging, attention mechanism, feature enhancement, multi-level feature extraction, real-time inference, urban scene segmentation
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.
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