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LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation.

, , , , , and . WACV, page 3255-3265. IEEE, (2023)

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Multimodal End-to-End Autonomous Driving., , , , and . IEEE Trans. Intell. Transp. Syst., 23 (1): 537-547 (2022)Semantic Monocular Depth Estimation Based on Artificial Intelligence., , , , and . IEEE Intell. Transp. Syst. Mag., 13 (4): 99-103 (2021)On the Metrics for Evaluating Monocular Depth Estimation., and . CoRR, (2023)DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation., , , , and . CVPR, page 15866-15877. IEEE, (2023)Monocular Depth Estimation by Learning from Heterogeneous Datasets., , , , and . Intelligent Vehicles Symposium, page 2176-2181. IEEE, (2018)Multimodal End-to-End Autonomous Driving., , , , and . CoRR, (2019)Monocular Depth Estimation for Autonomous Driving.. Autonomous University of Barcelona, Spain, (2022)LoopDA: Constructing Self-loops to Adapt Nighttime Semantic Segmentation., , , , , and . WACV, page 3255-3265. IEEE, (2023)Monocular Depth Estimation Through Virtual-World Supervision and Real-World SfM Self-Supervision., , , , and . IEEE Trans. Intell. Transp. Syst., 23 (8): 12738-12751 (2022)Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision., , , , and . CoRR, (2021)