Cited by 731Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningMultimodal Machine Learning Applications

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CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers

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Zhang, Jiaming, Liu, Huayao, Yang, Kailun, Hu, Xinxin, Liu, Ruiping, Stiefelhagen, Rainer·2023·IEEE Transactions on Intelligent Transportation Systems

DOI 10.1109/tits.2023.3300537

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Abstract

Scene understanding based on image segmentation is a crucial component of autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (${X}$-modality). However, covering a wide variety of sensors with a modality-agnostic model remains an unresolved problem due to variations in sensor characteristics among different modalities. Unlike previous modality-specific methods, in this work, we propose a unified fusion framework, CMX, for RGB-X semantic segmentation. To generalize well across different modalities, that often include supplements as well as uncertainties, a unified cross-modal interaction is crucial for modality fusion. Specifically, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate bi-modal features by leveraging the features from one modality to rectify the features of the other modality. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to perform sufficient exchange of long-range contexts before mixing. To verify CMX, for the first time, we unify five modalities complementary to RGB, i.e., depth, thermal, polarization, event, and LiDAR. Extensive experiments show that CMX generalizes well to diverse multi-modal fusion, achieving state-of-the-art performances on five RGB-Depth benchmarks, as well as RGB-Thermal, RGB-Polarization, and RGB-LiDAR datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish an RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. The source code of CMX is publicly available athttps://github.com/huaaaliu/RGBX_Semantic_Segmentation.

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Attention Is All You Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
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Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · arXiv · doi:10.48550/arxiv.1802.02611 · Cited by 20,063
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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

Show all 107 references

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Bui, L., Phung, S. L., Di, Y., Duong, S. T. M., & Bouzerdoum, A. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104793 · Cited by 2
SAMDFuse: Semantic-Aware Multi-scale Degradation-robust Fusion network for SAR and optical images
Wang, R., Li, Y., Zhang, E., Chen, W., & Wang, K. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.04.049 · Cited by 2
TUNI: Unifying Pre-Training and Fine-Tuning With Modality-Aware Mutual Learning and Rectification for RGB-T Semantic Segmentation
Guo, X., Guo, X., Liu, T., Deng, Z., Peng, Y., Li, X., & Zhou, W. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3701706 · Cited by 4
LiteMFT: Lightweight Multi-Modal Fine-Tuning for Semantic Segmentation
Guo, C., Zhang, Y., Zhang, M., Liu, H., & Li, W. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3702423 · Cited by 2
Self-citation
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He, M., Yang, Z., Zhang, G., Long, Y., & Song, H. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105109 · Cited by 2
Multi-modal hierarchical classification for power equipment defect detection
Bai, Y., Wang, L., Gao, W., & Ma, Y. (2024) · Journal of Image and Graphics · doi:10.11834/jig.230269 · Cited by 2
RGBD-based method for segmenting apparent pores within bridge towers
Zhang, Y., Chen, B., Li, Y., Wang, H., Tan, L., Wang, C., & Zhang, H. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad6897 · Cited by 2
STONet-S*: A Knowledge-Distilled Approach for Semantic Segmentation in Remote Sensing Images
Zhou, W., Yang, P., Qiu, W., & Qiang, F. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3446549 · Cited by 2
Enhancing Multimodal Fusion with Only Unimodal Data
Han, W., Geng, J., Deng, X., & Jiang, W. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10641451 · Cited by 2
BFRNet: Bimodal Fusion and Rectification Network for Remote Sensing Semantic Segmentation
Weng, Q., Lin, Y., Pan, Z., Lin, J., Chen, G., Chen, M., & Zhang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8493-6_35 · Cited by 2
Remote Sensing Image Scene Classification via Graph Template Enhancement and Supplementation Network With Dual-Teacher Knowledge Distillation
Zhou, W., Yang, P., Liu, Y., Cong, R., & Jiang, Q. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3490559 · Cited by 2
CFEINet: Cross-fusion and feature enhancement interaction network for RGB-D semantic segmentation
Ge, B., Lu, Y., Xia, C., Zhu, X., Zhang, M., Gao, M., & Chen, N. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105043 · Cited by 2
Semantic Co-Occurrence and Relationship Modeling for Remote Sensing Image Segmentation
Zhang, Y., Song, H., Wang, Q., Jin, P., & Shen, T. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3540789 · Cited by 2
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Gherold, V., Mandralis, I., Sihite, E. N., Salagame, A., Ramezani, A., & Gharib, M. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3572792 · Cited by 2
An efficient deep learning strategy for real-time semantic segmentation of trees for embedded systems
Leroy, P., Abisset‐Chavanne, E., Pommier, R., & Montemurro, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111516 · Cited by 2
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Zhou, J., Wu, Y., Xu, Y., & Liu, W. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00748-w · Cited by 2
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Zhou, Z., Wu, Z., Bolli, F., Boutteau, R., Yang, F., Timofte, R., Dominique Ginhac, B., & Delbrück, T. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128135 · Cited by 2
URNet: uncertainty-aware refinement network for event-based stereo depth estimation
Cheng, Y., Knoll, A., & Cao, H. (2025) · Visual Intelligence · doi:10.1007/s44267-025-00090-1 · Cited by 2
YOLO-FMEN: Pixel-Level Image Fusion-Based Nighttime Object Detection Network
Chen, Y.-T., & Da, F. (2025) · Chinese Control Conference (CCC) · doi:10.23919/ccc64809.2025.11179509 · Cited by 2
HLMamba: Hybrid Lightweight Mamba-Based Fusion Network for Dense Prediction of Remote Sensing Images
Zhou, W., Yang, P., & Liu, Y. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3590548 · Cited by 2

Authors: Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R.
Affiliations: Karlsruhe Institute of Technology (DE)
Nio (China) (CN)
Hunan University (CN)
ByteDance (China) (CN)
ORCID: Zhang, Jiaming
Yang, Kailun
Hu, Xinxin
Liu, Ruiping
Stiefelhagen, Rainer
Venue: IEEE Transactions on Intelligent Transportation Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2023-12
Volume: 24
Issue: 12
Pages: 14679-14694
ISSN: 1524-9050 (ISSN-L)
1558-0016
DOI: 10.1109/tits.2023.3300537
OpenAlex: W4386179772
Cited by: 731
References: 107
Citations by year: 2026: 225 · 2025: 268 · 2024: 178 · 2023: 53
FWCI: 65.71
Type: journal article
Language: en
Open access: green
arxiv.org
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 Intelligent Transportation Systems (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Federal Ministry of Labor and Social Affairs (BMAS) through the AccessibleMaps Project (01KM151112)
“KIT Future Fields” Project
Ministry of Science, Research and the Arts of Baden-Wurttemberg (MWK) through the Cooperative Graduate School Accessibility through AIBased Assistive Technology (BW6-03)
Federal Ministry of Education and Research (BMBF) through the Fellowship within the IFI Program of the German Academic Exchange Service
HoreKA@KIT Supercomputer Partition
Hangzhou SurImage Technology Company Ltd
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 › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords: cross-modal fusion, multimodal fusion, depth images, thermal images, polarization images, event cameras, LiDAR, transformer models

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