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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 -
Real-time 3D Traffic Cone Detection for Autonomous Driving
(2019)2019 IEEE Intelligent Vehicles Symposium (IV)Conference Paper -
Is Image Super-resolution Helpful for Other Vision Tasks?
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)Despite the great advances made in the field of image super-resolution (ISR) during the last years, the performance has merely been evaluated perceptually. Thus, it is still unclear whether ISR is helpful for other vision tasks. In this paper, we present the first comprehensive study and analysis of the usefulness of ISR for other vision applications. In particular, six ISR methods are evaluated on four popular vision tasks, namely edge ...Conference Paper -
Leveraging single for multi-target tracking using a novel trajectory overlap affinity measure
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)Multi-target tracking (MTT) is the task of localizing objects of interest in a video and associating them through time. Accurate affinity measures between object detections is crucial for MTT. Previous methods use simple affinity measures, based on heuristics, that are unable to track through occlusions and missing detections. To address this problem, this paper proposes a novel affinity measure by leveraging the power of single-target ...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 -
Fast algorithms for linear and kernel SVM+
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016)Conference Paper -
The Synthesizability of Texture Examples
(2014)Proceedings. 2014 IEEE Conference on Computer Vision and Pattern Recognition CVPR 2014Conference Paper -
PathTrack: Fast Trajectory Annotation with Path Supervision
(2017)2017 IEEE International Conference on Computer Vision (ICCV)Progress in Multiple Object Tracking (MOT) has been historically limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use it to produce a MOT dataset of unprecedented size. In our novel path supervision the annotator loosely follows the object with the cursor while watching the video, providing a path annotation for each object in the sequence. Our approach is able to turn such weak ...Conference Paper -
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
(2019)2019 IEEE/CVF International Conference on Computer Vision (ICCV)Most progress in semantic segmentation reports on daytime images taken under favorable illumination conditions. We instead address the problem of semantic segmentation of nighttime images and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions ...Conference Paper -
Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
(2018)2018 21st International Conference on Intelligent Transportation Systems (ITSC)This work addresses the problem of semantic image segmentation of nighttime scenes. Although considerable progress has been made in semantic image segmentation, it is mainly related to daytime scenarios. This paper proposes a novel method to progressive adapt the semantic models trained on daytime scenes, along with large-scale annotations therein, to nighttime scenes via the bridge of twilight time - the time between dawn and sunrise, ...Conference Paper