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.
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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
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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
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