626 citations · 788 across the 14 of their papers we have counts for
11 papers · 1 filter
VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions
Mingjia Li, Binhui Xie, Shuang Li +2
Generalizing models trained on normal visual conditions to target domains under adverse conditions is demanding in the practical systems. One prevalent solution is to bridge the do…
Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
Binhui Xie, Longhui Yuan, Shuang Li +2
Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network. However, r…
Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation
Shuang Li, Binhui Xie, Bin Zang +4
Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. Current state-of-the-art works sugg…
MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
Shuang Li, Kaixiong Gong, Chi Harold Liu +3
Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes…
AutoTrajectory: Label-free Trajectory Extraction and Prediction from Videos using Dynamic Points
Yuexin Ma, Xinge ZHU, Xinjing Cheng +3
Current methods for trajectory prediction operate in supervised manners, and therefore require vast quantities of corresponding ground truth data for training. In this paper, we pr…
ODE-CNN: Omnidirectional Depth Extension Networks
Xinjing Cheng, Peng Wang, Yanqi Zhou +2
Omnidirectional 360° camera proliferates rapidly for autonomous robots since it significantly enhances the perception ability by widening the field of view(FoV). However, correspon…
CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion
Xinjing Cheng, Peng Wang, Chenye Guan +1
Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is on…
Part-level Car Parsing and Reconstruction from Single Street View
Qichuan Geng, Hong Zhang, Xinyu Huang +5
Part information has been shown to be resistant to occlusions and viewpoint changes, which is beneficial for various vision-related tasks. However, we found very limited work in ca…
Learning Depth with Convolutional Spatial Propagation Network
Xinjing Cheng, Peng Wang, Ruigang Yang
Depth prediction is one of the fundamental problems in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn t…
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network
Xinjing Cheng, Peng Wang, Ruigang Yang
Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN)…
The ApolloScape Open Dataset for Autonomous Driving and its Application
Xinyu Huang, Peng Wang, Xinjing Cheng +3
Autonomous driving has attracted tremendous attention especially in the past few years. The key techniques for a self-driving car include solving tasks like 3D map construction, se…