activity
20182022
most citedRevisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning

43 citations · 88 across the 12 of their papers we have counts for

collaborators

23 papers

cs.LG2022

Online Attentive Kernel-Based Temporal Difference Learning

Guang Yang, Xingguo Chen, Shangdong Yang +3

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficie…

cs.CV20217 cited

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…

cs.CV2021

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…

cs.CV20201 cited

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…

cs.LG2020

Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective

Yinghuan Shi, Wanqi Yang, Kim-Han Thung +5

In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD pat…

cs.CV20202 cited

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…