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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 -
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 -
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 -
Warp Consistency for Unsupervised Learning of Dense Correspondences
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearance changes, which are ubiquitous in geometric and semantic matching tasks. Moreover, methods relying on synthetic training pairs often suffer from poor generalisation to real data.We propose Warp Consistency, an unsupervised ...Conference Paper -
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
(2021)2021 IEEE/CVF International Conference on Computer Vision (ICCV)We propose a deep reparametrization of the maximum a posteriori formulation commonly employed in multi-frame image restoration tasks. Our approach is derived by introducing a learned error metric and a latent representation of the target image, which transforms the MAP objective to a deep feature space. The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image ...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