Search
Results
-
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 -
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 -
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 -
Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo
(2022)2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural networks with excellent surface normal accuracy. Unlike previous uncalibrated deep PS networks, which are handcrafted and carefully tuned, we leverage differentiable neural architecture search (NAS) strategy to find uncalibrated PS architecture automatically. We begin ...Conference Paper