289 citations · 514 across the 44 of their papers we have counts for
18 papers · 1 filter
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Rui Gong, Yuhua Chen, Danda Pani Paudel +5
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advant…
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Lukas Hoyer, Dengxin Dai, Yuhua Chen +3
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a…
mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Rui Gong, Dengxin Dai, Yuhua Chen +2
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…
Depth Estimation from Monocular Images and Sparse Radar Data
Juan-Ting Lin, Dengxin Dai, Luc Van Gool
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 comprehen…
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
Ozan Unal, Luc Van Gool, Dengxin Dai
Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level ta…
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Shijie Li, Xieyuanli Chen, Yun Liu +3
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational reso…