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

Zhang, Jiaming; Liu, Huayao; Yang, Kailun; Hu, Xinxin; Liu, Ruiping; Stiefelhagen, Rainer

doi:10.1109/tits.2023.3300537

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

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

Open access

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

Open this paper in CoChat — free account Open PDF

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.

Integrity signals

Registry

Crossref · 10.1109/tits.2023.3300537

Cites

105 of 105 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

CMX, using a MiT-B4 backbone, achieves an mIoU of 82.6 on the Cityscapes dataset for 2.5D semantic segmentation.
“| 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]
A very common architectural choice is to adopt a multi-stream architecture for the encoder with a network branch processing each modality and additional network modules connecting the different branches that combine modality-specific features into fused ones and/or carry information across the branches.
“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]
CMX, using a MiT-B4 backbone, achieves an mIoU of 59.7 on the MSSSD/MF dataset for RGB + Thermal semantic segmentation.
“| 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]

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
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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
MizAR 60 for Mizar 50
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 · see citing works
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 · see citing works
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 · see citing works
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. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · 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) · arXiv · doi:10.48550/arxiv.1802.02611 · Cited by 20,063 · 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
Pyramid Scene Parsing Network
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438 · see citing works
Non-local Neural Networks
Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596 · see citing works
Dual Attention Network for Scene Segmentation
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., & Lu, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00326 · Cited by 7,351 · see citing works
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., & Fergus, R. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33715-4_54 · Cited by 5,760 · see citing works
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., & Shao, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00061 · Cited by 5,259 · see citing works
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021) · arXiv · doi:10.48550/arxiv.2105.15203 · Cited by 5,090 · see citing works
Deep High-Resolution Representation Learning for Visual Recognition
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983686 · Cited by 4,894 · see citing works
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368 · see citing works
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P. H. S., et al. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00681 · Cited by 4,008 · cited 1× · see citing works
“Recently, SETR [31] and Segmenter [32] directly adopt vision transformers [21], [22] as the backbone, which captures global context from very early layers.”
Background
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017) · arXiv · doi:10.48550/arxiv.1711.03938 · Cited by 3,718 · see citing works
CCNet: Criss-Cross Attention for Semantic Segmentation
Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., & Liu, W. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00069 · Cited by 3,045 · 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
Segmenter: Transformer for Semantic Segmentation
Strudel, R., Garcia, R. A., Laptev, I., & Schmid, C. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00717 · Cited by 2,254 · see citing works
Unified Perceptual Parsing for Scene Understanding
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., & Sun, J. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01228-1_26 · Cited by 2,201 · cited 2× · see citing works
“Specifically, in addition to MiT backbones, we experiment with DeepLabV3+ [120] and Swin transformer [24] backbones with UperNet [121] to construct CMX.”
Method
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. (2017) · 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.2017.667 · Cited by 2,073 · 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
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Gupta, S., Girshick, R., Arbeláez, P., & Malik, J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10584-0_23 · Cited by 1,528 · 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
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) · arXiv · doi:10.48550/arxiv.1604.01685 · Cited by 1,174 · see citing works
Learning a Discriminative Feature Network for Semantic Segmentation
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., & Sang, N. (2018) · 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.2018.00199 · Cited by 923 · 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
Gated-SCNN: Gated Shape CNNs for Semantic Segmentation
Takikawa, T., Acuna, D., Jampani, V., & Fidler, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00533 · Cited by 870 · see citing works
CGNet: A Light-Weight Context Guided Network for Semantic Segmentation
Wu, T., Tang, S., Zhang, R., Cao, J., & Zhang, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3042065 · Cited by 859 · see citing works
KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3D
Liao, Y., Xie, J., & Geiger, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3179507 · Cited by 770 · 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
Joint 2D-3D-Semantic Data for Indoor Scene UnderstandingPreprint
Armeni, I., (Sasha), A. S., Zamir, A., & Savarese, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.01105 · Cited by 622 · see citing works
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving
Prakash, A., Chitta, K., & Geiger, A. (2021) · 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/cvpr46437.2021.00700 · Cited by 616 · see citing works
Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., & Gool, L. V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00721 · Cited by 604 · see citing works
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Pohlen, T., Hermans, A., Mathias, M., & Leibe, B. (2017) · 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.2017.353 · Cited by 591 · 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
PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing
Xu, D., Ouyang, W., Wang, X., & Sebe, N. (2018) · 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.2018.00077 · Cited by 541 · see citing works
ACNET: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
Hu, X., Yang, K., Fei, L., & Wang, K. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803025 · Cited by 500 · see citing works
Self-citation
Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
Chen, X., Lin, K.-Y., Wang, J., Wu, W., Qian, C., Li, H., & Zeng, G. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58621-8_33 · Cited by 488 · see citing works
3D Graph Neural Networks for RGBD Semantic Segmentation
Qi, X., Liao, R., Jia, J., Fidler, S., & Urtasun, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.556 · Cited by 482 · see citing works
Fast-SCNN: Fast Semantic Segmentation NetworkPreprint
Poudel, R. P. K., Liwicki, S., & Cipolla, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04502 · Cited by 464 · see citing works
In Defense of Pre-Trained ImageNet Architectures for Real-Time Semantic Segmentation of Road-Driving Images
Oršić, M., Krešo, I., Bevandić, P., & Šegvić, S. (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.2019.01289 · Cited by 437 · see citing works
Rethinking Semantic Segmentation: A Prototype View
Zhou, T., Wang, W., Konukoğlu, E., & Goo, L. V. (2022) · 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/cvpr52688.2022.00261 · Cited by 414 · see citing works
3DMV: Joint 3D-Multi-view Prediction for 3D Semantic Scene Segmentation
Dai, A., & Nießner, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01249-6_28 · Cited by 347 · see citing works
GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation
Zhou, W., Liu, J., Lei, J., Yu, L., & Hwang, J.-N. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3109518 · Cited by 344 · see citing works
Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation
Zhang, Z., Cui, Z., Xu, C., Yan, Y., Sebe, N., & Yang, J. 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.2019.00423 · Cited by 339 · see citing works
Efficient RGB-D Semantic Segmentation for Indoor Scene Analysis
Seichter, D., Kohler, M., Lewandowski, B., Wengefeld, T., & Gross, H.-M. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561675 · Cited by 330 · see citing works
ACFNet: Attentional Class Feature Network for Semantic Segmentation
Zhang, F., Chen, Y., Li, Z., Hong, Z., Liu, J., Ma, F., Han, J., & Ding, E. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00690 · Cited by 324 · 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
RDFNet: RGB-D Multi-level Residual Feature Fusion for Indoor Semantic Segmentation
Lee, S., Park, S., & Hong, K.-S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.533 · Cited by 294 · see citing works
Self-supervised model adaptation for multimodal semantic segmentation
Valada, A., Mohan, R., & Burgard, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01188-y · Cited by 293 · see citing works
Efficient Attention: Attention with Linear Complexities
Shen, Z., Zhang, M., Zhao, H., Yi, S., & Li, H. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00357 · Cited by 282 · see citing works
Multimodal Token Fusion for Vision Transformers
Wang, Y., Chen, X., Cao, L., Huang, W., Sun, F., & Wang, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01187 · Cited by 272 · 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
PST900: 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
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Bachmann, R., Mizrahi, D., Atanov, A., & Zamir, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19836-6_20 · Cited by 268 · see citing works
Locality-Sensitive Deconvolution Networks with Gated Fusion for RGB-D Indoor Semantic Segmentation
Cheng, Y., Cai, R., Li, Z., Zhao, X., & Huang, K. (2017) · 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.2017.161 · Cited by 242 · see citing works
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection
Pang, Y., Zhao, X., Zhang, L., & Lu, H. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3234702 · Cited by 238 · see citing works
ABMDRNet: Adaptive-weighted Bi-directional Modality Difference Reduction Network for RGB-T Semantic Segmentation
Zhang, Q., Zhao, S., Luo, Y., Zhang, D., Huang, N., & Han, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00266 · Cited by 225 · see citing works
Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., & Tan, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01597 · Cited by 216 · see citing works
Omnivore: A Single Model for Many Visual Modalities
Girdhar, R., Singh, M., Ravi, N., van der Maaten, L., Joulin, A., & Misra, I. (2022) · 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/cvpr52688.2022.01563 · Cited by 212 · see citing works
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
Cao, J., Leng, H., Lischinski, D., Cohen-Or, D., Tu, C., & Li, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00700 · Cited by 206 · see citing works
Training data-efficient image transformers & distillation through attention
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., & Jégou, H. (2020) · arXiv · doi:10.48550/arxiv.2012.12877 · Cited by 182 · see citing works
HRFormer: High-Resolution Transformer for Dense Prediction
Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., & Wang, J. (2021) · arXiv · doi:10.48550/arxiv.2110.09408 · Cited by 178 · see citing works
Spatial Information Guided Convolution for Real-Time RGBD Semantic Segmentation
Chen, L.-Z., Lin, Z., Wang, Z., Yang, Y., & Cheng, M.-M. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049332 · Cited by 177 · see citing works
Real-Time Fusion Network for RGB-D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road-Driving Images
Sun, L., Yang, K., Hu, X., Hu, W., & Wang, K. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3007457 · Cited by 174 · see citing works
Self-citation
FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation
Deng, F., Feng, H., Liang, M., Wang, H., Yang, Y., Gao, Y., Chen, J., Hu, J., Guo, X., & Lam, T. L. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636084 · Cited by 170 · see citing works
Combining Events and Frames Using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
Gehrig, D., Rüegg, M., Gehrig, M., Hidalgo‐Carrió, J., & Scaramuzza, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3060707 · Cited by 169 · see citing works
EV-SegNet: Semantic Segmentation for Event-Based Cameras
Alonso, Í., & Murillo, A. C. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00205 · Cited by 168 · see citing works
Cascaded Feature Network for Semantic Segmentation of RGB-D Images
Lin, D., Chen, G., Cohen‐Or, D., Heng, P., & Huang, H. F. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.147 · Cited by 155 · see citing works
Deep Polarization Cues for Transparent Object Segmentation
Kalra, A., Taamazyan, V., Rao, S. K., Venkataraman, K., Raskar, R., & Kadambi, A. (2020) · 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/cvpr42600.2020.00863 · Cited by 148 · see citing works
CANet: Co-attention network for RGB-D semantic segmentation
Zhou, H., Qi, L., Huang, H. Y., Xu, Y., Wan, Z., & Wen, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108468 · Cited by 130 · see citing works
Bidirectional Projection Network for Cross Dimension Scene Understanding
Hu, W., Zhao, H., Jiang, L., Jia, J., & Wong, T. (2021) · 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/cvpr46437.2021.01414 · Cited by 118 · see citing works
Recurrent Scene Parsing with Perspective Understanding in the Loop
Kong, S., & Fowlkes, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00106 · Cited by 114 · see citing works
Polarization-driven semantic segmentation via efficient attention-bridged fusion
Xiang, K., Yang, K., & Wang, K. (2021) · Optics Express · doi:10.1364/oe.416130 · Cited by 111 · see citing works
Self-citation
ESS: Learning Event-Based Semantic Segmentation from Still Images
Sun, Z., Messikommer, N., Gehrig, D., & Scaramuzza, D. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_20 · Cited by 104 · see citing works
Non-Local Aggregation for RGB-D Semantic Segmentation
Zhang, G., Xue, J., Xie, P., Yang, S., & Wang, G. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3066071 · Cited by 101 · see citing works
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving
Zhou, W., Berrío, J. S., Worrall, S., & Nebot, E. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2909066 · Cited by 99 · see citing works
Attention fusion network for multi-spectral semantic segmentation
Xu, J., Lu, K., & Wang, H. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.015 · Cited by 99 · see citing works
Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance
Zhang, J., Yang, K., Constantinescu, A., Peng, K., Müller, K., & Stiefelhagen, R. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3161141 · Cited by 96 · see citing works
Self-citation
Trans4Trans: Efficient Transformer for Transparent Object Segmentation to Help Visually Impaired People Navigate in the Real World
Zhang, J., Yang, K., Constantinescu, A., Peng, K., Müller, K., & Stiefelhagen, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00202 · Cited by 87 · see citing works
Self-citation
RGB-D Semantic Segmentation and Label-Oriented Voxelgrid Fusion for Accurate 3D Semantic Mapping
Shi, W., Xu, J., Zhu, D., Zhang, G., Wang, X., Li, J., & Zhang, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3056726 · Cited by 73 · see citing works
Learning Deep Multimodal Feature Representation with Asymmetric Multi-layer Fusion
Wang, Y., Sun, F., Lu, M., & Yao, A. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413621 · Cited by 68 · see citing works
ISSAFE: Improving Semantic Segmentation in Accidents by Fusing Event-based Data
Zhang, J., Yang, K., & Stiefelhagen, R. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636109 · Cited by 65 · see citing works
Self-citation
StructToken: Rethinking Semantic Segmentation With Structural Prior
Lin, F., Liang, Z., Wu, S., He, J., Chen, K., & Tian, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3252807 · Cited by 60 · see citing works
Omnisupervised Omnidirectional Semantic Segmentation
Yang, K., Hu, X., Fang, Y., Wang, K., & Stiefelhagen, R. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3023331 · Cited by 59 · see citing works
Self-citation
Hyperfusion-Net: Hyper-densely reflective feature fusion for salient object detection
Zhang, P., Liu, W., Lei, Y., & Lu, H. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.05.012 · Cited by 54 · see citing works
DHFNet: dual-decoding hierarchical fusion network for RGB-thermal semantic segmentation
Cai, Y., Zhou, W., Zhang, L., Yu, L., & Luo, T. (2023) · The Visual Computer · doi:10.1007/s00371-023-02773-6 · Cited by 53 · see citing works
HRFuser: A Multi-Resolution Sensor Fusion Architecture for 2D Object Detection
Brödermann, T., Sakaridis, C., Dai, D., & Gool, L. V. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422432 · Cited by 46 · see citing works
Malleable 2.5D Convolution: Learning Receptive Fields Along the Depth-Axis for RGB-D Scene Parsing
Xing, Y., Wang, J., & Zeng, G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58529-7_33 · Cited by 45 · see citing works
Two-Stage Cascaded Decoder for Semantic Segmentation of RGB-D Images
Yue, Y., Zhou, W., Lei, J., & Yu, L. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3084855 · Cited by 43 · see citing works
Exploring Event-Driven Dynamic Context for Accident Scene Segmentation
Zhang, J., Yang, K., & Stiefelhagen, R. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3134828 · Cited by 37 · see citing works
Self-citation
Semantic Segmentation by Early Region Proxy
Zhang, Y., Pang, B., & Lu, C. (2022) · 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/cvpr52688.2022.00132 · Cited by 34 · see citing works
Gated-Residual Block for Semantic Segmentation Using RGB-D Data
Qian, Y., Deng, L., Li, T., Wang, C., & Yang, M. Z. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107672 · Cited by 31 · see citing works
NLFNet: Non-Local Fusion Towards Generalized Multimodal Semantic Segmentation across RGB-Depth, Polarization, and Thermal Images
Yan, R., Yang, K., & Wang, K. (2021) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio54168.2021.9739390 · Cited by 29 · see citing works
Self-citation
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth ImagesPreprint
Fooladgar, F., & Shohreh Kasaei, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.11691 · Cited by 25 · see citing works
A multimodal vision sensor for autonomous driving
Sun, D., Huang, X. M., & Yang, K. (2019) · Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies · doi:10.1117/12.2535552 · Cited by 23 · see citing works
Self-citation
ConvMLP: Hierarchical Convolutional MLPs for VisionPreprint
Li, J., Hassani, A., Walton, S., & Shi, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.04454 · Cited by 23 · see citing works
Depth-Adapted CNN for RGB-D Cameras
Wu, Z., Allibert, G., Stolz, C., & Demonceaux, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_24 · Cited by 19 · see citing works
Multilevel Cross-Aware RGBD Indoor Semantic Segmentation for Bionic Binocular Robot
Shi, W., Zhu, D., Zhang, G., Xu, J., Wang, X., Chen, L., Li, J., & Zhang, X. (2020) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2020.3009527 · Cited by 14 · see citing works
Real-time Pedestrian Localization and State Estimation Using Moving Horizon Estimation
Mohammadbagher, E., Bhatt, N. P., Hashemi, E., Fi̇dan, B., & Khajepour, A. (2020) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc45102.2020.9294306 · Cited by 12 · see citing works
Tube-Embedded Transformer for Pixel Prediction
Zhang, X., Zhang, S., Cui, Z., Li, Z., Xie, J., & Yang, J. J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3147664 · Cited by 10 · see citing works

Flagged first, then by citations

2 of the 107 references couldn’t be matched to a known paper.

Sorted by citations

Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity
Tang, L., Zhang, H., Xu, H., & Ma, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101870 · Cited by 396 · see citing works
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 · see citing works
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 · see citing works
Self-citation
Cross-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 215 · 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
Self-citation
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
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmarks for Autonomous Driving on Water Surfaces
Yao, S., Guan, R., Wu, Z., Ni, Y., Huang, Z., Liu, R. W., Yue, Y., Ding, W., Lim, E. G., Seo, H., et al. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3415772 · Cited by 133 · see citing works
Self-citation
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking
Hou, X., Xing, J., Qian, Y., Guo, Y., Xin, S., Chen, J., Tang, K., Wang, M., Jiang, Z., Liu, L., et al. (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/cvpr52733.2024.02507 · Cited by 134 · see citing works
TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
Zhang, X., Zhang, X., Wang, J., Ying, J., Sheng, Z., Yu, H., Li, C., & Shen, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3443455 · Cited by 111 · see citing works
RGB-D and Thermal Sensor Fusion: A Systematic Literature Review
Brenner, M., Reyes, N. H., Sušnjak, T., & Barczak, A. L. C. (2023) · IEEE Access · doi:10.1109/access.2023.3301119 · Cited by 107 · see citing works
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene Parsing
Dong, S., Zhou, W., Xu, C., & Yan, W. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3306368 · Cited by 107 · 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
Misaligned Visible-Thermal Object Detection: A Drone-Based Benchmark and Baseline
Song, K., Xue, X., Wen, H., Ji, Y., Yan, Y., & Meng, Q. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3398429 · Cited by 92 · see citing works
Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation
Huang, W., Zhou, Y., He, X., & Lv, C. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3312453 · Cited by 85 · see citing works
Transformer Fusion and Pixel-Level Contrastive Learning for RGB-D Salient Object Detection
Wu, J., Hao, F., Liang, W., & Xu, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3275308 · Cited by 80 · see citing works
OmniVec: Learning robust representations with cross modal sharing
Srivastava, S., & Sharma, G. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00127 · Cited by 80 · see citing works
A Multistage Information Complementary Fusion Network Based on Flexible-Mixup for HSI-X Image Classification
Wang, J., Zhang, M., Li, W., & Tao, R. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3300903 · Cited by 74 · see citing works
Glass Segmentation With RGB-Thermal Image Pairs
Huo, D., Wang, J., Qian, Y., & Yang, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3256762 · Cited by 73 · see citing works
IF-USOD: Multimodal information fusion interactive feature enhancement architecture for underwater salient object detection
Yuan, G., Song, J., & Li, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102806 · Cited by 76 · see citing works
Progressive Adjacent-Layer coordination symmetric cascade network for semantic segmentation of Multimodal remote sensing images
Xiaomin, F., Zhou, W., Qian, X., & Yan, W. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121999 · Cited by 71 · see citing works
TransKD: Transformer Knowledge Distillation for Efficient Semantic Segmentation
Liu, R., Yang, K., Roitberg, A., Zhang, J., Peng, K., Liu, H., Wang, Y., & Stiefelhagen, R. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3455416 · Cited by 81 · see citing works
Self-citation
DRMF: Degradation-Robust Multi-Modal Image Fusion via Composable Diffusion Prior
Tang, L., Deng, Y., Yi, X., Yan, Q., Yuan, Y., & Ma, J. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681064 · Cited by 62 · see citing works
Part-Whole Relational Fusion Towards Multi-Modal Scene Understanding
Liu, Y., Li, C., Xu, S., & Han, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02393-8 · Cited by 59 · see citing works
Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
MultiSenseSeg: A Cost-Effective Unified Multimodal Semantic Segmentation Model for Remote Sensing
Wang, Q., Chen, W., Huang, Z., Tang, H., & Yang, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3390750 · Cited by 58 · see citing works
MMFlood: A Multimodal Dataset for Flood Delineation From Satellite Imagery
Montello, F., Arnaudo, E., & Rossi, C. (2022) · IEEE Access · doi:10.1109/access.2022.3205419 · Cited by 57 · see citing works
AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation
Du, S., Wang, W., Guo, R., Wang, R., & Tang, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00756 · Cited by 59 · see citing works
LMFNet: Lightweight Multimodal Fusion Network for high-resolution remote sensing image segmentation
Wang, T., Chen, G., Zhang, X., Liu, C., Wang, J., Tan, X., Zhou, W., & He, C. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111579 · Cited by 58 · see citing works
ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification
Zhang, P., Peng, B., Lu, C., Huang, Q., & Liu, D. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.09.025 · Cited by 54 · see citing works
DHFNet: dual-decoding hierarchical fusion network for RGB-thermal semantic segmentation
Cai, Y., Zhou, W., Zhang, L., Yu, L., & Luo, T. (2023) · The Visual Computer · doi:10.1007/s00371-023-02773-6 · Cited by 53 · see citing works
UTFNet: Uncertainty-Guided Trustworthy Fusion Network for RGB-Thermal Semantic Segmentation
Wang, Q., Yin, C., Song, H., Shen, T., & Gu, Y. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3322452 · Cited by 52 · see citing works
Removal Then Selection: A Coarse-to-Fine Fusion Perspective for RGB-Infrared Object Detection
Zhao, T. C., Yuan, M., Jiang, F., Wang, N., & Wei, X. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3638627 · Cited by 52 · see citing works
An interactive fusion attention-guided network for ground surface hot spring fluids segmentation in dual-spectrum UAV images
Yi, S., Chen, M., Yuan, X., Si, G., & Wang, J. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.01.022 · Cited by 52 · see citing works
DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation
Yin, B.-W., Cao, J.-L., Cheng, M., & Hou, Q. (2025) · 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/cvpr52734.2025.01802 · Cited by 51 · see citing works
Transformer fusion for indoor RGB-D semantic segmentation
Wu, Z., Zhou, Z., Allibert, G., Stolz, C., Demonceaux, C., & Ma, C. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104174 · Cited by 50 · see citing works
IGT: Illumination-guided RGB-T object detection with transformers
Chen, K., Liu, J., & Zhang, H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110423 · Cited by 50 · see citing works
Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
Wang, Y., Albrecht, C. M., Braham, N. A. A., Liu, C., Xiong, Z., & Zhu, X. X. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-73397-0_17 · Cited by 49 · see citing works
Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers
Zhu, Z., Hou, J., & Wu, D. O. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02015 · Cited by 48 · see citing works
3D-MMFN: Multi-level multimodal fusion network for 3D industrial image anomaly detection
Asad, M., Azeem, W., Malik, A. A., Jiang, H., Ali, A., Yang, J., & Liu, W. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103284 · Cited by 48 · see citing works
Mask-DiFuser: A Masked Diffusion Model for Unified Unsupervised Image Fusion
Tang, L., Li, C., & Ma, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3609323 · Cited by 47 · see citing works
A Comprehensive Review of Modern Object Segmentation Approaches
Wang, Y., Ahsan, U., Li, H., & Hagen, M. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000097 · Cited by 49 · see citing works
A Transformer-based multi-modal fusion network for semantic segmentation of high-resolution remote sensing imagery
Liu, Y., Gao, K., Wang, H., Yang, Z., Wang, P., Ji, S., Huang, Y., Zhu, Z., & Zhao, X. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104083 · Cited by 43 · see citing works
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Dong, S., Feng, Y., Yang, Q., Huang, Y., Liu, D., & Fan, H. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801872 · Cited by 46 · see citing works
MMSFormer: Multimodal Transformer for Material and Semantic Segmentation
Reza, M. K., Prater-Bennette, A., & Asif, M. S. (2024) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2024.3389812 · Cited by 42 · see citing works
RoadFormer+: Delivering RGB-X Scene Parsing Through Scale-Aware Information Decoupling and Advanced Heterogeneous Feature Fusion
Huang, J., Li, J., Ning, J., Sun, Y., Liu, C., Chen, Q., & Fan, R. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3448251 · Cited by 42 · see citing works
SELMA: SEmantic Large-Scale Multimodal Acquisitions in Variable Weather, Daytime and Viewpoints
Testolina, P., Barbato, F., Michieli, U., Giordani, M., Zanuttigh, P., & Zorzi, M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3257086 · Cited by 41 · see citing works
On 3D Reconstruction Using RGB-D Cameras
Tychola, K. A., Tsimperidis, I., & Papakostas, G. A. (2022) · Digital · doi:10.3390/digital2030022 · Cited by 41 · see citing works
U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation
Li, B., Zhang, D., Zhao, Z., Gao, J., & Li, X. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111801 · Cited by 41 · see citing works
FreeFusion: Infrared and Visible Image Fusion via Cross Reconstruction Learning
Zhao, W., Cui, H., Wang, H., He, Y., & Lu, H. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3572599 · Cited by 40 · see citing works
Complementary Random Masking for RGB-Thermal Semantic Segmentation
Shin, U., Lee, K., Kweon, I. S., & Oh, J. (2024) · 2024 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra57147.2024.10611200 · Cited by 49 · see citing works
“The primary research direction is designing a multi-modal fusion module [44], [46], [8], [21] that can effectively combine the information from both RGB and thermal modalities to improve the accuracy of semantic segmentation.”
BackgroundMethodInfluential
CFFormer: A Cross-Fusion Transformer Framework for the Semantic Segmentation of Multisource Remote Sensing Images
Zhao, J., Zhang, M., Zhou, Z., Wang, Z., Lang, F., Shi, H., & Zheng, N. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3507274 · Cited by 39 · see citing works
UTLNet: Uncertainty-Aware Transformer Localization Network for RGB-Depth Mirror Segmentation
Zhou, W., Cai, Y., Zhang, L., Yan, W., & Yu, L. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3323890 · Cited by 38 · see citing works
IPNet: Polarization-based Camouflaged Object Detection via dual-flow network
Wang, X., Ding, J., Zhang, Z., Xu, J., & Gao, J. Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107303 · Cited by 38 · see citing works
FTransDeepLab: Multimodal Fusion Transformer-Based DeepLabv3+ for Remote Sensing Semantic Segmentation
Feng, H., Hu, Q., Zhao, P., Wang, S., Ai, M., Zheng, D., & Liu, T. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3553478 · Cited by 38 · see citing works
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation
Maheshwari, H., Liu, Y.-C., & Kira, Z. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00106 · Cited by 37 · see citing works
Graph Attention Guidance Network With Knowledge Distillation for Semantic Segmentation of Remote Sensing Images
Zhou, W., Xiaomin, F., Yan, W., Shan, S., Jiang, Q., & Hwang, J. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3311480 · Cited by 36 · see citing works
Multimodal Feature-Guided Pretraining for RGB-T Perception
Ouyang, J., Jin, P., & Wang, Q. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3454054 · Cited by 34 · see citing works
Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things
Zhou, W., Jian, B., & Liu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3537281 · Cited by 33 · see citing works
RFIENet: RGB-thermal feature interactive enhancement network for semantic segmentation of insulator in backlight scenes
Li, B., Wang, T., Zhai, Y., & Yuan, J. (2022) · Measurement · doi:10.1016/j.measurement.2022.112177 · Cited by 33 · see citing works
UnionFormer: Unified-Learning Transformer with Multi-View Representation for Image Manipulation Detection and Localization
Li, S., Ma, W., Guo, J., Xu, S., Li, B., & Zhang, X. (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/cvpr52733.2024.01190 · Cited by 33 · see citing works
TGF: Multiscale transformer graph attention network for multi-sensor image fusion
Mustafa, H. T., Shamsolmoali, P., & Lee, I. H. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121789 · Cited by 31 · see citing works
CloudSeg: A multi-modal learning framework for robust land cover mapping under cloudy conditions
Xu, F., Shi, Y., Yang, W. T., Xia, G.-S., & Zhu, X. X. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.06.001 · Cited by 30 · see citing works
Difference-Complementary Learning and Label Reassignment for Multimodal Semi-Supervised Semantic Segmentation of Remote Sensing Images
Han, W., Jiang, W. R., Geng, J., & Wang, M. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3526064 · Cited by 30 · see citing works
CFNet: Automatic multi-modal brain tumor segmentation through hierarchical coarse-to-fine fusion and feature communication
Cheng, Y., Zheng, Y., & Wang, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106876 · Cited by 28 · see citing works
Light Transport Induced Domain Adaptation for Semantic Segmentation in Thermal Infrared Urban Scenes
Chen, J., Liu, Z., Jin, D., Wang, Y., Yang, F., & Bai, X. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3194931 · Cited by 28 · see citing works
EISNet: A Multi-Modal Fusion Network for Semantic Segmentation With Events and Images
Xie, B., Deng, Y., Shao, Z., & Li, Y. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3380255 · Cited by 28 · see citing works
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event Camera
Cao, J., Xu, Z., Lyu, Y., Wang, J., Xu, R., & Wang, L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611705 · Cited by 28 · see citing works
DACFusion: Dual Asymmetric Cross-Attention guided feature fusion for multispectral object detection
Qian, J., Qiao, B., Zhang, Y., Liu, T., Wang, S., Wu, G., & Han, D. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129913 · Cited by 27 · see citing works
Exploring Multi-modal Fusion for Image Manipulation Detection and Localization
Triaridis, K., & Mezaris, V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53311-2_15 · Cited by 27 · see citing works
A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving
Li, G., Lin, Y., Ouyang, D., Li, S., Luo, X., Qu, X., Pi, D., & Li, S. E. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3332350 · Cited by 27 · see citing works
Global feature-based multimodal semantic segmentation
Gao, S., Yang, X., Jiang, L. M., Fu, Z., & Du, J. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110340 · Cited by 26 · see citing works
CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes
Brödermann, T., Sakaridis, C., Fu, Y., & Gool, L. V. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3536218 · Cited by 26 · see citing works
Elevation Information-Guided Multimodal Fusion Robust Framework for Remote Sensing Image Segmentation
Fan, J., Li, J., Hua, Z., Zhang, F., & Zhang, C. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3350593 · Cited by 25 · see citing works
Deep learning based object detection from multi-modal sensors: an overview
Liu, Y., Meng, S., Wang, H., & Liu, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16275-z · Cited by 26 · see citing works
DHFNet: Decoupled Hierarchical Fusion Network for RGB-T dense prediction tasks
Chen, H., Wang, Z., Qin, H., & Mu, X. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127594 · Cited by 24 · see citing works
SCAU-net: 3D self-calibrated attention U-Net for brain tumor segmentation
Liu, D., Sheng, N., Han, Y., Hou, Y., Liu, B., Zhang, J., & Zhang, Q. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08872-8 · Cited by 24 · see citing works
Self-citation
Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection
Cao, H., Zhang, Z., Xia, Y., Li, X., Xia, J., Chen, G., & Knoll, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72907-2_10 · Cited by 23 · see citing works
MGF-GCN: Multimodal interaction Mamba-aided graph convolutional fusion network for semantic segmentation of remote sensing images
Zhao, Y., Qiu, L., Yang, Z., Chen, Y., & Zhang, Y. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103150 · Cited by 23 · see citing works
An Energy-Efficient, Unified CNN Accelerator for Real-Time Multi-Object Semantic Segmentation for Autonomous Vehicle
Jung, J., Kim, S., Jang, W., Seo, B., & Lee, K. J. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3349588 · Cited by 23 · see citing works
Polarimetric Imaging for Robot Perception: A Review
Taglione, C., Mateo, C. M., & Stolz, C. (2024) · Sensors · doi:10.3390/s24144440 · Cited by 23 · see citing works
UAMFDet: Acoustic‐Optical Fusion for Underwater Multi‐Modal Object Detection
Chen, H., Wang, Z., Qin, H., & Mu, X. (2024) · Journal of Field Robotics · doi:10.1002/rob.22432 · Cited by 23 · see citing works
Specificity-Guided Cross-Modal Feature Reconstruction for RGB-Infrared Object Detection
Sun, X., Zhu, Y., & Huang, H. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3495028 · Cited by 23 · see citing works
Steering Angle-Guided Multimodal Fusion Lane Detection for Autonomous Driving
Gong, Y., Zhang, X., Lu, J., Jiang, X., Wang, Z., Liu, H., Li, Z., Wang, L., Yang, Q., & Wu, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3507536 · Cited by 23 · see citing works
Self-citation
MiLNet: Multiplex Interactive Learning Network for RGB-T Semantic Segmentation
Liu, J., Liu, H., Li, X., Ren, J., & Xu, X. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3544484 · Cited by 23 · see citing works
Self-citation
DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT
Chen, X., Zhou, B., Xie, H., Guo, X., Zhang, J., Duncan, J. S., Miller, E. J., Sinusas, A. J., Onofrey, J. A., & Liu, C. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102840 · Cited by 22 · see citing works
Self-citation
Region-adaptive and context-complementary cross modulation for RGB-T semantic segmentation
Peng, F., Ding, Z., Chen, Z., Wang, G., Hui, T., Liu, S., & Shi, H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110092 · Cited by 22 · see citing works
Towards Source-Free Domain Adaptive Semantic Segmentation via Importance-Aware and Prototype-Contrast Learning
Cao, Y., Zhang, H., Lu, X. B., Xiao, Z., Yang, K., & Wang, Y. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3383157 · Cited by 22 · see citing works
Self-citation
SiMultiF: A Remote Sensing Multimodal Semantic Segmentation Network With Adaptive Allocation of Modal Weights for Siamese Structures in Multiscene
Cui, S., Chen, W., Xiong, W., Xu, X. J., Shi, X., & Li, C. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3553713 · Cited by 22 · see citing works
STSNet: A cross-spatial resolution multi-modal remote sensing deep fusion network for high resolution land-cover segmentation
Yu, B., Li, J., & Huang, X. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102689 · Cited by 21 · see citing works
EMTrack: Efficient Multimodal Object Tracking
Liu, C., Guan, Z., Lai, S., Liu, Y., Lu, H., & Wang, D. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3494725 · Cited by 23 · see citing works
When Pedestrian Detection Meets Multi-modal Learning: Generalist Model and Benchmark Dataset
Zhang, Y., Zeng, W., Jin, S., Qian, C., Luo, P., & Liu, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73195-2_25 · Cited by 22 · see citing works
Late better than early: A decision-level information fusion approach for RGB-Thermal crowd counting with illumination awareness
Cheng, J., Feng, C., Xiao, Y., & Cao, Z. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127888 · Cited by 21 · see citing works
MutualFormer: Multi-modal Representation Learning via Cross-Diffusion Attention
Wang, X., Wang, X., Jiang, B., Tang, J., & Luo, B. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02067-x · Cited by 20 · see citing works
MSMA: A multi-stage and multi-attention algorithm for the classification of multimodal skin lesions
Shu, C., Yu, L., Tian, S., & Shi, X. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106180 · Cited by 19 · see citing works
Understanding Dark Scenes by Contrasting Multi-Modal Observations
Dong, X., & Yokoya, N. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00089 · Cited by 20 · see citing works
PolyMaX: General Dense Prediction with Mask Transformer
Yang, X., Yuan, L., Wilber, M. J., Sharma, A., Gu, X., Qiao, S., Debats, S. R., Wang, H., Adam, H., Sirotenko, M., et al. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00109 · Cited by 19 · see citing works
Dual-modal non-local context guided multi-stage fusion for indoor RGB-D semantic segmentation
Guo, X., Ma, W., Liang, F., & Mi, Q. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124598 · Cited by 19 · see citing works
Image semantic segmentation of indoor scenes: A survey
Velasteguí, R., Tatarchenko, M., Karaoğlu, S., & Gevers, T. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104102 · Cited by 21 · see citing works
LCH: fast RGB-D salient object detection on CPU via lightweight convolutional network with hybrid knowledge distillation
Wang, B., Zhang, F., & Zhao, Y. (2023) · The Visual Computer · doi:10.1007/s00371-023-02898-8 · Cited by 18 · see citing works
Towards efficient RGB-T semantic segmentation via feature generative distillation strategy
Zhao, S., Wang, J., Zhang, Q., & Han, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103282 · Cited by 18 · see citing works

RGB-X Object Detection via Scene-Specific Fusion Modules
Deevi, S. A., Lee, C., Gan, L., Nagesh, S., Pandey, G., & Chung, S. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00720 · Cited by 38 · see citing works
A novel RGB-X semantic segmentation network with cross-modal feature reweighting and local-global feature aggregation
Zhiwei, Z., Zhuang, Y., Liu, Y., & Zhang, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113334
Multimodal Fusion of RGB and Complementary Modalities for Semantic SegmentationPreprint
Cao, H., Cheng, Y., He, M., Li, M., Liu, Y., & Knoll, A. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2610.17800
SegRGB-X: General RGB-X Semantic Segmentation ModelPreprint
Liu, J., Xu, Y., Zhou, X., Song, R., Zimmer, W. M. X., Knoll, A., & Cao, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7397681
RGB-X segmentation method based on parallel feature correction and fusion
Yang, Q., & Yan, W. (2026) · International Conference on Electronic Technology and Energy Systems (ETES) · doi:10.1117/12.3112654

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 · 2022: 7
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

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

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

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks