The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Abstract
Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation. This is challenging as the two input spaces are heterogeneous and can be impacted differently by domain shift. In xMUDA, modalities learn from each other through mutual mimicking, disentangled from the segmentation objective, to prevent the stronger modality from adopting false predictions from the weaker one. We evaluate on new UDA scenarios including day-to-night, country-to-country and dataset-to-dataset, leveraging recent autonomous driving datasets. xMUDA brings large improvements over uni-modal UDA on all tested scenarios, and is complementary to state-of-the-art UDA techniques. Code is available at https://github.com/valeoai/xmuda.
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
“Multiple decoders can also be used but it is a less common choice [121].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [121]
In the citing paper
“| xMUDA [121] | SparseConvNet3D [125] ResNet34 [16] | 49.1 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [121]
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
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 10,099
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
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
KPConv: Flexible and Deformable Convolution for Point Clouds
Thomas, H., Qi, C. R., Deschaud, J., Marcotegui, B., Goulette, F., & Guibas, L. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,400
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., & Darrell, T. (2017) · arXiv · doi:10.48550/arxiv.1711.03213 · Cited by 2,656
Research works printed but not linked: 2
- lee, 2013, Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, ICML Workshop
- jakob, 2019, A2D2 AEV Autonomous Driving Dataset
Cited by
Sorted by citations
Items 101–200 of 223
COLA: COarse-LAbel Multisource LiDAR Semantic Segmentation for Autonomous Driving
Sanchez, J., Deschaud, J., & Goulette, F. (2025) · IEEE Transactions on Robotics · doi:10.1109/tro.2025.3543302 · Cited by 14
Khindkar, V., Arora, C., Balasubramanian, V. N., Subramanian, A., Saluja, R., & Jawahar, C. V. (2022) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv51458.2022.00045 · Cited by 13
Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation
Zhang, B., Wang, Z., Ling, Y., Guan, Y., Zhang, S., & Li, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25448 · Cited by 15
Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds
Peng, X., Chen, R., Qiao, F., Kong, L., Liu, Y., Sun, Y., Wang, T., Zhu, X., & Ma, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72775-7_4 · Cited by 27
Wozniak, M. K., Hansson, M., Thiel, M., & Jensfelt, P. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3487489 · Cited by 13
Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching
Bian, Y., Hui, L., Qian, J., & Xie, J. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981603 · Cited by 13
CrossMatch: Source-Free Domain Adaptive Semantic Segmentation via Cross-Modal Consistency Training
Yin, Y., Hu, W., Liu, Z., Wang, G., Xiang, S., & Zimmermann, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01991 · Cited by 13
Relative Norm Alignment for Tackling Domain Shift in Deep Multi-modal Classification
Planamente, M., Plizzari, C., Peirone, S. A., Barbara Caputo, B., & Bottino, A. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-01998-9 · Cited by 14
Domain Adaptive LiDAR Point Cloud Segmentation via Density-Aware Self-Training
Xiao, A., Huang, J., Liu, K., Guan, D., Zhang, X., & Lu, S. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3386865 · Cited by 12
CLIP2UDA: Making Frozen CLIP Reward Unsupervised Domain Adaptation in 3D Semantic Segmentation
Wu, Y., Xing, M., Zhang, Y., Xie, Y., & Qu, Y. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680582 · Cited by 13
Kothandaraman, D., Chandra, R., & Manocha, D. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00442 · Cited by 12
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions
Alonso, Í., Riazuelo, L., Montesano, L., & Murillo, A. C. (2021) · International Conference on Informatics in Control, Automation and Robotics (ICINCO) · doi:10.5220/0010610703300337 · Cited by 16
A Survey of Efficient Deep Learning Models for Moving Object Segmentation
Hou, B., Liu, Y., Ling, N., Ren, Y., & Liu, L. (2023) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000140 · Cited by 12
Image2Point: 3D Point-Cloud Understanding with Pretrained 2D ConvNets.Preprint
Xu, C., Yang, S., Zhai, B., Wu, B., Yue, X., Zhan, W., Vajda, P., Keutzer, K., & Tomizuka, M. (2021) · arXiv (Cornell University) · Cited by 14
Dai, M., Xing, S., Xu, Q., Li, P., Pan, J., Zhang, G., & Wang, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3364181 · Cited by 12
Yang, L., Hoyer, L., Weber, M., Fischer, T., Dai, D., Leal-Taixé, L., Pollefeys, M., Cremers, D., & Gool, L. V. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72933-1_19 · Cited by 17
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class DistributionsPreprint
Alonso, Í., Riazuelo, L., Montesano, L., & Murillo, A. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.12239 · Cited by 13
Huch, S., & Lienkamp, M. (2023) · Sensors · doi:10.3390/s23249913 · Cited by 10
Component recognition of ISAR targets via multimodal feature fusion
Chenxuan, L. I., Weigang, Z. H. U., Qu, W., Ma, F., & Rundong, W. A. N. G. (2024) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2024.06.031 · Cited by 10
4D Contrastive Superflows are Dense 3D Representation Learners
Xu, X., Kong, L., Shuai, H., Zhang, W., Pan, L., Chen, K., Liu, Z., & Liu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73232-4_4 · Cited by 10
360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation
Reichardt, L., Ebert, N., & Wasenmüller, O. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00115 · Cited by 10
LiDARNet: A Boundary-Aware Domain Adaptation Model for Lidar Point Cloud Semantic SegmentationPreprint
Jiang, P., & Saripalli, S. (2020) · arXiv.org · doi:10.48550/arxiv.2003.01174 · Cited by 16
Multisensor Fusion Network for Unstructured Scene Segmentation With Surface Normal Incorporated
Feng, Y., Li, X., Ni, P., Liu, X., & Jiang, T. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3370255 · Cited by 9
Duan, Y., Meng, L., Meng, Y., Zhu, J., Zhang, J., Zhang, J., & Liu, X. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3472751 · Cited by 9
Wu, Y., Xing, M., Zhang, Y., Xie, Y., & Qu, Y. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3545971 · Cited by 9
Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds
Kasireddy, V., & Akinci, B. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5041 · Cited by 9
Continual Road-Scene Semantic Segmentation Via Feature-Aligned Symmetric Multi-Modal Network
Barbato, F., Camuffo, E., Milani, S., & Zanuttigh, P. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647930 · Cited by 9
Capitalizing on RGB-FIR Hybrid Imaging for Road Detection
Zhang, Y., Xie, J., Álvarez, J. M., Xu, C., Yang, J. J., & Kong, H. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3129692 · Cited by 9
EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS
Xie, Y., Schindler, K., Tian, J., & Zhu, X. X. (2021) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b2-2021-247-2021 · Cited by 8
Unsupervised Domain Adaptive 3-D Detection With Data Adaption From LiDAR Point Cloud
Zhang, D., Wang, X., Zheng, Z., & Liu, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3226570 · Cited by 8
Teachers in Concordance for Pseudo-Labeling of 3D Sequential Data
Gebrehiwot, A. H., Vacek, P., Hurych, D., Zimmermann, K., Pérez, P., & Svoboda, T. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3226029 · Cited by 8
SUMMIT: Source-Free Adaptation of Uni-Modal Models to Multi-Modal Targets
Simons, C., Raychaudhuri, D. S., Ahmed, S. M., You, S. O., Karydis, K., & Roy–Chowdhury, A. K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00120 · Cited by 8
Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation
Vobecký, A., Hurych, D., Siméoni, O., Gidaris, S., Bursuc, A., Pérez, P., & Šivic, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-024-02320-3 · Cited by 10
Unsupervised 3D Object Detection Domain Adaptation Based on Pseudo-Label Variance Regularization
Wang, K., Zhou, P., Hu, M., & Lǚ, J. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3538770 · Cited by 7
FAST3D: Flow-Aware Self-Training for 3D Object DetectorsPreprint
Fruhwirth-Reisinger, C., Opitz, M., Possegger, H., & Bischof, H. (2022) · doi:10.31219/osf.io/x93wg · Cited by 7
Sun, M., Rui, T., Liu, J., Wang, D., Yang, C., & Zheng, N. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00201-4 · Cited by 6
Robust 3D Semantic Segmentation Method Based on Multi-Modal Collaborative Learning
Ni, P., Xu, L., Wang, X., Zhou, X., Jiang, T., & Hu, W. (2024) · Remote Sensing · doi:10.3390/rs16030453 · Cited by 6
Scene point cloud understanding and reconstruction technologies in 3D space
Gong, J., Lou, Y., Liu, F., Zhang, Z., Chen, H., Zhang, Z., Tan, X., Xie, Y., & Ma, L. (2023) · Journal of Image and Graphics · doi:10.11834/jig.230004 · Cited by 11
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
Gebrehiwot, A. H., Hurych, D., Zimmermann, K., Pérez, P., & Svoboda, T. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341446 · Cited by 6
DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
Man, Y., Gui, L.-Y., & Wang, Y.-X. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341473 · Cited by 6
Tian, J., Saddik, A. E., Xu, X., Li, D., Cao, Z., & Shen, H. T. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3362948 · Cited by 6
Cross-Modal Match for Language Conditioned 3D Object Grounding
Zhang, Y., Hu, R., Li, R., Qu, Y., Xie, Y., & Li, X. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28566 · Cited by 7
DDFN: a depth-differential fusion network for multi-focus image
Jiang, L., Fan, H., & Li, J. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12075-z · Cited by 6
Luo, H., Chen, Z., Ye, F., Huang, T., He, H., & Hu, W. (2025) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2025.07.021 · Cited by 5
WHU-STree: A multi-modal benchmark dataset for street tree inventory
Ding, R., Chen, Z., Fan, W. L., Chen, L. F., Xiao, H., Zeng, Y., Dong, Z., & Yang, B. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.02.011 · Cited by 5
Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation
Chen, Y., Xu, Z., Huang, X., Zhao, S., Jiang, X., Gao, X., & Gao, X. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3535350 · Cited by 5
Corral-Soto, E. R., Rochan, M., He, Y. Y., Chen, X., Aich, S., & Bingbing, L. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186529 · Cited by 5
Structure-aware fusion network for 3D scene understanding
Yan, H., Lv, Y., & Liong, V. E. (2021) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2021.07.012 · Cited by 5
Industrial object detection with multi-modal SSD: closing the gap between synthetic and real images
Cohen, J., Crispim-Junior, C. F., Chiappa, J.-M., & Tougne, L. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15367-0 · Cited by 5
HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering
Xu, J., & Waslander, S. L. (2023) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv60082.2023.00035 · Cited by 5
Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation
Liu, Y., Zhang, W., Zhao, G., Zhu, J., Vasilakos, A. V., & Wang, L. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3336611 · Cited by 5
Multi-Modal Meta-Transfer Fusion Network for Few-Shot 3D Model Classification
Zhou, H., Liu, A.-A., Zhang, C., Zhu, P., Zhang, Q.-Y., & Kankanhalli, M. (2023) · International Journal of Computer Vision · doi:10.1007/s11263-023-01905-8 · Cited by 4
Cross-Domain Incremental Feature Learning for ALS Point Cloud Semantic Segmentation With Few Samples
Dai, M., Xing, S., Xu, Q. Q., Li, P., Pan, J., & Wang, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3524212 · Cited by 6
Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation
Chen, Y., Zhao, S., Ding, C., Tang, L., Wang, C., & Tao, D. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612320 · Cited by 4
Chen, J., Zhang, Y., Ma, F., Huang, K., Tan, Z., Qi, Y., & Li, J. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3469269 · Cited by 4
Cho, J., Ha, J., Song, H., Jang, S., & Jo, K. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110024 · Cited by 4
Unsupervised Domain Adaptive Point Cloud Semantic Segmentation
Bian, Y., Xie, J., & Qian, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02375-0_21 · Cited by 4
Enhancing LiDAR Point Cloud Segmentation with Synthetic Data
Inan, B. A., Rondao, D., & Aouf, N. (2023) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med59994.2023.10185741 · Cited by 4
Calib3D: Calibrating Model Preferences for Reliable 3D Scene Understanding
Kong, L., Xu, X., Cen, J., Zhang, W., Pan, L., Chen, K., & Liu, Z. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00198 · Cited by 9
Liang, Z., Yin, H., Liang, M., Du, Q., Yang, Y., & Huang, H. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.01892 · Cited by 3
Enhanced Prototypical Learning for Unsupervised Domain Adaptation in LiDAR Semantic Segmentation
Yi, E., Yang, J., & Kim, J. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811838 · Cited by 3
LiOn-XA: Unsupervised Domain Adaptation via LiDAR-Only Cross-Modal Adversarial Training
Kreutz, T., Lemke, J., Mühlhäuser, M., & Guinea, A. S. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801916 · Cited by 3
A Model for Urban Environment Instance Segmentation with Data Fusion
Du, K., Meng, J., Meng, X., Wang, S., & Yang, J. (2023) · Sensors · doi:10.3390/s23136141 · Cited by 3
Unsupervised Learning of ALS Point Clouds for 3-D Terrain Scene Clustering
Zhang, J., Hu, X., & Dai, H. (2021) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2020.3047096 · Cited by 3
Kong, X., Xia, S., Liu, N., & Wei, M. (2023) · The Visual Computer · doi:10.1007/s00371-023-02799-w · Cited by 3
Weijler, L., Mirza, M. J., Sick, L., Ekkazan, C., & Hermosilla, P. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00120 · Cited by 2
Domain Adaptation-Based Crossmodal Knowledge Distillation for 3D Semantic Segmentation
Kang, J., Wang, J., & Luo, D. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128498 · Cited by 2
Multi-Modality Test-Time Adaptation for Semantic Segmentation in Robotic Perception
Liu, Y., Zhu, H., Zhang, Y., Lei, Y., & Guo, Y.-L. (2025) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra55743.2025.11127565 · Cited by 2
Enhanced Event-Based Dense Stereo via Cross-Sensor Knowledge Distillation
Zhang, H., Zhang, Y., Li, J., Zhu, L., Lv, M., Zhu, Y., Liu, Y., & Ji, X. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00516 · Cited by 2
Learning Semantic Segmentation on Unlabeled Real-World Indoor Point Clouds via Synthetic Data
Song, Y., Sun, Z., Wu, Y., Sun, Y., Luo, S., & Li, Q. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956612 · Cited by 2
Shi, M., Cao, H., Xie, L., & Yang, J. (2022) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv57592.2022.10004265 · Cited by 2
Xie, Y., & Tian, J. (2023) · Joint Urban Remote Sensing Event (JURSE) · doi:10.1109/jurse57346.2023.10144187 · Cited by 2
Ren, X., Guo, N., Zhu, Z., & Jiang, X. (2024) · Computers & Graphics · doi:10.1016/j.cag.2024.104045 · Cited by 2
Chen, T.-Y., Yang, L., Chuang, T.-Y., & Lai, S. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00810 · Cited by 2
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Kong, L., Xu, X., Liu, Y.-C., Cen, J., Chen, R.-N., Zhang, W.-W., Pan, L., Chen, K., & Liu, Z.-W. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3617126 · Cited by 23
A Comprehensive Research and Strategy of Transfer Learning for Image Segmentation
Wang, M., Wu, F., & Zhao, J. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-70665-4_152 · Cited by 2
Camera and LiDAR Fusion for Point Cloud Semantic Segmentation
AbdelKader, A., & Moustafa, M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2394-4_45 · Cited by 2
Multimodal Fusion and Data Augmentation for 3D Semantic Segmentation
He, D., Abid, F., & Kim, J.-H. (2022) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas55662.2022.10003729 · Cited by 2
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial DatasetsPreprint
Gong, R., Dai, D., Chen, Y., Li, W., & Gool, L. V. (2020) · arXiv (Cornell University) · doi:10.3929/ethz-b-000554635 · Cited by 2
Density-aware global–local attention network for point cloud segmentation
Li, C., Zhang, P., Zhang, J., & Wu, Y. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105822 · Cited by 1
Xie, Y., Zhao, Y., Zhang, Q., Zhang, L., Li, T., & Zhang, W. (2024) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio64047.2024.10907464 · Cited by 1
Cop, K. P., & Trzcinski, T. P. (2025) · IEEE Access · doi:10.1109/access.2025.3562671 · Cited by 1
SAM-Guided Pseudo Label Enhancement for Multi-Modal 3D Semantic Segmentation
Yang, M., Lu, J., & Kim, H.-S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128411 · Cited by 1
Active Cross-Modal Domain Adaptation
Yao, X., Xiao, P., Gao, J., Yuan, Z., Wu, X. Y., & Xu, C. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3604968 · Cited by 1
Sun, M., Rui, T., Liu, J., Wang, D., & Yang, C. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3605592 · Cited by 1
Exploring Modality Guidance to Enhance VFM-Based Feature Fusion for UDA in 3D Semantic Segmentation
Spoecklberger, J., Lin, W. R., Hermosilla, P., Doveh, S., Possegger, H., & Mirza, M. J. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00465 · Cited by 1
Navarro-Pérez, A., Bacca, E., & Bravo, E. F. C. (2026) · Robotics · doi:10.3390/robotics15050088 · Cited by 1
3D Reconstruction of 2D Semantic Segmentation Using Sensor Fusion Based Trans-Unet
Lee, Y., Jeon, J., Yoon, J., & Song, K. (2023) · Transactions of Korean Society of Automotive Engineers · doi:10.7467/ksae.2023.31.4.255 · Cited by 1
SFPN: segmentation-based feature pyramid network for multi-focus image fusion
Wu, P., Jiang, L., Li, Y., Fan, H., & Li, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15342-9 · Cited by 1
Wang, Z., Arablouei, R., Liu, J., Borges, P., Bishop-Hurley, G. J., & Heaney, N. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00256 · Cited by 1
Back to Reality: Learning Data-Efficient 3D Object Detector With Shape Guidance
Xu, X., Wang, Z., Zhou, J., & Lu, J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3328880 · Cited by 1
Crossmodal Few-shot 3D Point Cloud Semantic Segmentation via View Synthesis
Zhao, Z., Cai, P., Zhang, C., Li, X., & Wang, S. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681428 · Cited by 1
2D-3D Attention and Entropy for Pose Robust 2D Facial Recognition
Peace, J. B., Hu, S., & Riggan, B. S. (2025) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg61629.2025.11099278 · Cited by 1
Deep Mutual Learning Among Partially Labeled Datasets for Multi-Organ Segmentation
Liu, X., Qu, L., Xie, Z., Shi, Y., & Song, Z. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3614853 · Cited by 1
DIFL:Multi-Source Domain Adaptation for Point Clouds Using Domain-Invariant-Feature-LearningPreprint
Firkat, E., Huang, Y., Zhu, B., Mijit, T., Zhu, J., & Hamdulla, A. (2023) · doi:10.36227/techrxiv.23306399
Cardace, A., Conti, A., Ramirez, P. Z., Spezialetti, R., Salti, S., & Stefano, L. D. (2023) · IEEE Access · doi:10.1109/access.2023.3304542
Cross-Modal and Cross-Domain Knowledge Transfer for Label-Free 3D Segmentation
Zhang, J., Yang, H., Wu, D.-J., Keung, J., Li, X., Zhu, X., & Ma, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8435-0_37
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications
Liu, X., Zhang, C., Wang, G., Zhang, R., & Ji, X. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611208 · Cited by 2
Zhang, X., Li, Y., Sheng, H., & Zhang, X. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15250
Metadata
- Authors
- Jaritz, M., Vu, T.-H., de Charette, R., Wirbel, E., & Perez, P.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12602-12611
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035750252
- Cited by
- 224
- References
- 37
- Citations by year
- 2026: 15 · 2025: 35 · 2024: 38 · 2023: 57 · 2022: 42 · 2021: 28 · 2020: 8
- FWCI
- 12.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- unsupervised domain adaptation, cross-modal learning, 3D semantic segmentation, point clouds, 2D images, domain shift, disentangled representation learning, autonomous driving
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
