24 citations · 24 across the 3 of their papers we have counts for
6 papers
Zero-Shot Instance Segmentation
Ye Zheng, Jiahong Wu, Yongqiang Qin +2
Deep learning has significantly improved the precision of instance segmentation with abundant labeled data. However, in many areas like medical and manufacturing, collecting suffic…
BiOpt: Bi-Level Optimization for Few-Shot Segmentation
Jinlu Liu, Liang Song, Yongqiang Qin
Few-shot segmentation is a challenging task that aims to segment objects of new classes given scarce support images. In the inductive setting, existing prototype-based methods focu…
Prototype Refinement Network for Few-Shot Segmentation
Jinlu Liu, Yongqiang Qin
Few-shot segmentation targets to segment new classes with few annotated images provided. It is more challenging than traditional semantic segmentation tasks that segment known clas…
Generalized Adaptation for Few-Shot Learning
Liang Song, Jinlu Liu, Yongqiang Qin
Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constra…
Prototype Rectification for Few-Shot Learning
Jinlu Liu, Liang Song, Yongqiang Qin
Few-shot learning requires to recognize novel classes with scarce labeled data. Prototypical network is useful in existing researches, however, training on narrow-size distribution…
Accurate Face Detection for High Performance
Faen Zhang, Xinyu Fan, Guo Ai +3
Face detection has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs). Its central issue in recent years is how to improve the detectio…