The DOI matches its Crossref record; 106 of the 107 references it cites were found in a registry or an index.
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
Open accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“| CMX [99] | MiT-B4 [27] | 82.6 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [99]
In the citing paper
“A very common architectural choice is to adopt a multi-stream architecture for the encoder with a network branch processing each modality (e.g., a two-stream architecture for RGB and depth) and additional network modules connecting the different branches that combine modality-specific features into fused ones and/or carry information across the branches [98,99,101].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [99]
In the citing paper
“| CMX [99] | MiT-B4 [27] | 59.7 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [99]
Citations
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
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
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
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
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
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
Research works printed but not linked: 1
- Dosovitskiy, An image is worth 16✗16 words: Transformers for image recognition at scale, 1
Cited by
Sorted by citations
Tang, L., Zhang, H., Xu, H., & Ma, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101870 · Cited by 418
TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization
Guillaro, F., Cozzolino, D., Sud, A., Dufour, N., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01974 · Cited by 275
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
Self-citationCross-Modal Transformers for Infrared and Visible Image Fusion
Park, S. H., Vien, A. G., & Lee, C. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3289170 · Cited by 219
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
Self-citation
Metadata
- 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)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2023-12
- Volume
- 24
- Issue
- 12
- Pages
- 14679-14694
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- 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
- 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
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
