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Simultaneously using multimodal inputs from multiple sensors to train segmentors is intuitively advantageous but practically challenging.
Q. Zhang, S. Zhao, Y. Luo, D. Zhang, N. Huang, and J. Han, “Abmdrnet: Adaptive-weighted bi-directional modality difference reduction network for rgb-t semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2021
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L.-Z. Chen, Z. Lin, Z. Wang, Y.-L. Yang, and M.-M. Cheng, “Spatial information guided convolution for real-time rgbd semantic segmentation,” IEEE Transactions on Image Processing
2021
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E. Xie, W. Wang, Z. Yu, A. Anandkumar, J. M. Alvarez, and P. Luo, “Segformer: Simple and efficient design for semantic segmentation with transformers,” Advances in neural information processing systems
2021
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Y. Wang, X. Chen, L. Cao, W. Huang, F. Sun, and Y. Wang, “Multimodal token fusion for vision transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
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X. Peng, Y. Wei, A. Deng, D. Wang, and D. Hu, “Balanced multimodal learning via on-the-fly gradient modulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2022
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Y. Huang, J. Lin, C. Zhou, H. Yang, and L. Huang, “Modality competition: What makes joint training of multi-modal network fail in deep learning? (provably),” in International Conference on Machine Learning
2022
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J. Zhang, H. Liu, K. Yang, X. Hu, R. Liu, and R. Stiefelhagen, “Cmx: Cross-modal fusion for rgb-x semantic segmentation with transformers,” IEEE Transactions on intelligent transportation systems
2023
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J. Li, H. Dai, H. Han, and Y. Ding, “Mseg3d: Multi-modal 3d semantic segmentation for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
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X. Zheng, J. Zhu, Y. Liu, Z. Cao, C. Fu, and L. Wang, “Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
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J. Zhang, R. Liu, H. Shi, K. Yang, S. Reiß, K. Peng, H. Fu, K. Wang, and R. Stiefelhagen, “Delivering arbitrary-modal semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
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T. Broedermann, C. Sakaridis, D. Dai, and L. Van Gool, “Hrfuser: A multi-resolution sensor fusion architecture for 2d object detection,” in IEEE International Conference on Intelligent Transportation Systems
2023
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S. Wei, C. Luo, and Y. Luo, “Mmanet: Margin-aware distillation and modality-aware regularization for incomplete multimodal learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
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Y. Man, L.-Y. Gui, and Y.-X. Wang, “Bev-guided multi-modality fusion for driving perception,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
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M. Kleinman, A. Achille, and S. Soatto, “Critical learning periods for multisensory integration in deep networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Cited alongside, same era.
Y. Wang, Q. Mao, H. Zhu, J. Deng, Y. Zhang, J. Ji, H. Li, and Y. Zhang, “Multi-modal 3d object detection in autonomous driving: a survey,” International Journal of Computer Vision
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
R. Liu, J. Zhang, K. Peng, Y. Chen, K. Cao, J. Zheng, M. S. Sarfraz, K. Yang, and R. Stiefelhagen, “Fourier prompt tuning for modality-incomplete scene segmentation,” in IEEE Intelligent Vehicles Symposium
2024
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J. Zhou, X. Zheng, Y. Lyu, and L. Wang, “Eventbind: Learning a unified representation to bind them all for event-based open-world understanding,” in Proceedings of the European Conference on Computer Vision
2024
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X. Zheng and L. Wang, “Eventdance: Unsupervised source-free cross-modal adaptation for event-based object recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
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X. Zheng, P. Zhou, A. V. Vasilakos, and L. Wang, “Semantics distortion and style matter: Towards source-free uda for panoramic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
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2023
Cited alongside, same era.
H. Wang, C. Ma, J. Zhang, Y. Zhang, J. Avery, L. Hull, and G. Carneiro, “Learnable cross-modal knowledge distillation for multi-modal learning with missing modality,” in International Conference on Medical Image Computing and Computer-Assisted Intervention
2023
Cited alongside, same era.
X. Zheng, P. Y. Zhou, A. V. Vasilakos, and L. Wang, “360sfuda++: Towards source-free uda for panoramic segmentation by learning reliable category prototypes,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2024
Cited alongside, same era.
2024
Cited alongside, same era.
X. Zheng, Y. Luo, C. Fu, K. Liu, and L. Wang, “Transformer-cnn cohort: Semi-supervised semantic segmentation by the best of both students,” in 2024 IEEE International Conference on Robotics and Automation (ICRA)
2024
Cited alongside, same era.
Y. Lyu, X. Zheng, J. Zhou, and L. Wang, “Unibind: Llm-augmented unified and balanced representation space to bind them all,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Cited alongside, same era.
X. Zheng, Y. Lyu, and L. Wang, “Learning modality-agnostic representation for semantic segmentation from any modalities,” in Proceedings of the European Conference on Computer Vision
2024
Cited alongside, same era.
X. Zheng, Y. Lyu, J. Zhou, and L. Wang, “Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation,” in Proceedings of the European Conference on Computer Vision
2024
Cited alongside, same era.
T. Brödermann, D. Bruggemann, C. Sakaridis, K. Ta, O. Liagouris, J. Corkill, and L. Van Gool, “Muses: The multi-sensor semantic perception dataset for driving under uncertainty,” in Proceedings of the European Conference on Computer Vision
2024
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2024
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Y. Zhang, P. E. Latham, and A. M. Saxe, “Understanding unimodal bias in multimodal deep linear networks,” in International Conference on Machine Learning
2024
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H. Maheshwari, Y.-C. Liu, and Z. Kira, “Missing modality robustness in semi-supervised multi-modal semantic segmentation,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision
2024
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2025
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2025
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2025
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