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.
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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
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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
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