43 citations · 88 across the 12 of their papers we have counts for
17 papers · 1 filter
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
Yinghuan Shi, Jian Zhang, Tong Ling +5
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel…
Mining Latent Classes for Few-shot Segmentation
Lihe Yang, Wei Zhuo, Lei Qi +2
Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel clas…
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
Zheng Gu, Chuanqi Dong, Jing Huo +2
Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the…
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
Wen Ji, Kelei He, Jing Huo +2
Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. However, unlike the facial photo attribute datasets that have a quantity…
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi +1
Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current wor…
Asymmetric Distribution Measure for Few-shot Learning
Wenbin Li, Lei Wang, Jing Huo +3
The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings.…