433 citations · 771 across the 52 of their papers we have counts for
6 papers · 1 filter
LGNN: A Context-aware Line Segment Detector
Quan Meng, Jiakai Zhang, Qiang Hu +2
We present a novel real-time line segment detection scheme called Line Graph Neural Network (LGNN). Existing approaches require a computationally expensive verification or postproc…
Confidence-aware Adversarial Learning for Self-supervised Semantic Matching
Shuaiyi Huang, Qiuyue Wang, Xuming He
In this paper, we aim to address the challenging task of semantic matching where matching ambiguity is difficult to resolve even with learned deep features. We tackle this problem…
Towards Purely Unsupervised Disentanglement of Appearance and Shape for Person Images Generation
Hongtao Yang, Tong Zhang, Wenbing Huang +2
There have been a fairly of research interests in exploring the disentanglement of appearance and shape from human images. Most existing endeavours pursuit this goal by either usin…
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
Shuailin Li, Chuyu Zhang, Xuming He
Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for buildin…
Part-aware Prototype Network for Few-shot Semantic Segmentation
Yongfei Liu, Xiangyi Zhang, Songyang Zhang +1
Few-shot semantic segmentation aims to learn to segment new object classes with only a few annotated examples, which has a wide range of real-world applications. Most existing meth…
Learning Context-aware Task Reasoning for Efficient Meta-reinforcement Learning
Haozhe Wang, Jiale Zhou, Xuming He
Despite recent success of deep network-based Reinforcement Learning (RL), it remains elusive to achieve human-level efficiency in learning novel tasks. While previous efforts attem…