162 citations · 244 across the 9 of their papers we have counts for
9 papers
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
Liang Xiao, Jiaolong Xu, Dawei Zhao +3
Data augmentation has been widely used to improve generalization in training deep neural networks. Recent works show that using worst-case transformations or adversarial augmentati…
ORFD: A Dataset and Benchmark for Off-Road Freespace Detection
Chen Min, Weizhong Jiang, Dawei Zhao +4
Freespace detection is an essential component of autonomous driving technology and plays an important role in trajectory planning. In the last decade, deep learning-based free spac…
Trajectory Prediction for Autonomous Driving with Topometric Map
Jiaolong Xu, Liang Xiao, Dawei Zhao +2
State-of-the-art autonomous driving systems rely on high definition (HD) maps for localization and navigation. However, building and maintaining HD maps is time-consuming and expen…
Attentional Graph Neural Network for Parking-slot Detection
Chen Min, Jiaolong Xu, Liang Xiao +3
Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning…
Self-supervised Domain Adaptation for Computer Vision Tasks
Jiaolong Xu, Liang Xiao, Antonio M. Lopez
Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques…
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Jiaolong Xu, Peng Wang, Heng Yang +1
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent d…
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Zhang Li, Zheyu Hu, Jiaolong Xu +10
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome. This study aims to assess which deep learning models perform be…
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example
Antonio M. Lopez, Jiaolong Xu, Jose L. Gomez +2
Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing…
Hierarchical Adaptive Structural SVM for Domain Adaptation
Jiaolong Xu, Sebastian Ramos, David Vazquez +1
A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is bei…