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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 -
Discriminative Learning of Apparel Features
(2015)2015 14th IAPR International Conference on Machine Vision Applications (MVA)Conference Paper -
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VConference Paper -
Structured Output SVM Prediction of Apparent Age, Gender and Smile From Deep Features
(2016)Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognision Workshop (CVPRW 2016)Conference Paper -
Architectural Decomposition for 3D Landmark Building Understanding
(2016)2016 IEEE Winter Conference on Applications of Computer Vision (WACV)Decomposing 3D building models into architectural elements is an essential step in understanding their 3D structure. Although we focus on landmark buildings, our approach generalizes to arbitrary 3D objects. We formulate the decomposition as a multi-label optimization that identifies individual elements of a landmark. This allows our system to cope with noisy, incomplete, outlier-contaminated 3D point clouds. We detect three types of ...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 -
Differentiable Multi-Granularity Human Representation Learning for Instance-Aware Human Semantic Parsing
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)To address the challenging task of instance-aware human part parsing, a new bottom-up regime is proposed to learn category-level human semantic segmentation as well as multi-person pose estimation in a joint and end-to-end manner. It is a compact, efficient and powerful framework that exploits structural information over different human granularities and eases the difficulty of person partitioning. Specifically, a dense-to-sparse projection ...Conference Paper -
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision ...Conference Paper -
Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
(2020)Lecture Notes in Computer Science ~ Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IIConference Paper -
CompositeTasking: Understanding Images by Spatial Composition of Tasks
(2021)2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)We define the concept of CompositeTasking as the fusion of multiple, spatially distributed tasks, for various aspects of image understanding. Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network. To facilitate CompositeTasking, we introduce a novel task conditioning model – a single encoder-decoder network that ...Conference Paper