26 citations · 50 across the 5 of their papers we have counts for
6 papers
Gaussian Belief Propagation Network for Depth Completion
Jie Tang, Pingping Xie, Jian Li +1
Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning methods have achieved state-of-the-art (SOTA), effectiv…
Bilateral Propagation Network for Depth Completion
Jie Tang, Fei-Peng Tian, Boshi An +2
Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image. Current state-of-the-art (SOTA) methods are predominantly propagat…
Design of Novel Loss Functions for Deep Learning in X-ray CT
Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare +4
Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of dat…
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
Yanan Zheng, Jing Zhou, Yujie Qian +7
The few-shot natural language understanding (NLU) task has attracted much recent attention. However, prior methods have been evaluated under a disparate set of protocols, which hin…
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
Jing Zhou, Yanan Zheng, Jie Tang +2
Most previous methods for text data augmentation are limited to simple tasks and weak baselines. We explore data augmentation on hard tasks (i.e., few-shot natural language underst…
Learning Guided Convolutional Network for Depth Completion
Jie Tang, Fei-Peng Tian, Wei Feng +2
Dense depth perception is critical for autonomous driving and other robotics applications. However, modern LiDAR sensors only provide sparse depth measurement. It is thus necessary…