10 citations · 10 across the 2 of their papers we have counts for
6 papers · 1 filter
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…
Hierarchical Attention Networks for Medical Image Segmentation
Fei Ding, Gang Yang, Jinlu Liu +5
The medical image is characterized by the inter-class indistinction, high variability, and noise, where the recognition of pixels is challenging. Unlike previous self-attention bas…
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…
Imagination Based Sample Construction for Zero-Shot Learning
Gang Yang, Jinlu Liu, Xirong Li
Zero-shot learning (ZSL) which aims to recognize unseen classes with no labeled training sample, efficiently tackles the problem of missing labeled data in image retrieval. Nowaday…