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

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