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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIIIThis work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation ...Conference Paper -
Depth Estimation from Monocular Images and Sparse Radar Data
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)In this paper, we explore the possibility of achieving a more accurate depth estimation by fusing monocular images and Radar points using a deep neural network. We give a comprehensive study of the fusion between RGB images and Radar measurements from different aspects and proposed a working solution based on the observations. We find that the noise existing in Radar measurements is one of the main key reasons that prevents one from ...Conference Paper -
ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Level 5 autonomy for self-driving cars requires a robust visual perception system that can parse input images under any visual condition. However, existing semantic segmentation datasets are either dominated by images captured under normal conditions or are small in scale. To address this, we introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. ...Conference Paper -
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensive. In this paper, we tackle this problem by simulating physically accurate fog into clear-weather scenes, so that the abundant existing real datasets captured in clear weather can be repurposed for our task. Our contributions are twofold: 1) We develop a ...Conference Paper -
Hyperspectral Image Super-Resolution with RGB Image Super-Resolution as an Auxiliary Task
(2022)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)This work studies Hyperspectral image (HSI) super-resolution (SR). HSI SR is characterized by high-dimensional data and a limited amount of training examples. This raises challenges for training deep neural networks that are known to be data hungry. This work addresses this issue with two contributions. First, we observe that HSI SR and RGB image SR are correlated and develop a novel multi-tasking network to train them jointly so that the ...Conference Paper -
End-to-End Urban Driving by Imitating a Reinforcement Learning Coach
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that demand dense on-policy supervision. On the contrary, automated experts that leverage privileged information can efficiently generate large scale on-policy and off-policy demonstrations. However, existing automated experts for urban driving make heavy use of hand-crafted ...Conference Paper -
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)One challenge of object recognition is to generalize to new domains, to more classes and/or to new modalities. This necessitates methods to combine and reuse existing datasets that may belong to different domains, have partial annotations, and/or have different data modalities. This paper formulates this as a multi-source domain adaptation and label unification problem, and proposes a novel method for it. Our method consists of a ...Conference Paper -
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)omain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervision from auxiliary tasks (such as depth estimation) has the potential to heal this shift because many visual tasks are closely related to each other. However, such a supervision is not always available. In this work, we leverage the guidance from self-supervised ...Conference Paper -
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains. In this work, we propose a principled meta-learning based approach to OCDA for semantic segmentation, MOCDA, by modeling the unlabeled target domain continuously. Our approach consists of four key steps. First, ...Conference Paper -
End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners
(2018)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2018Conference Paper