output
20202023
most citedLearning Representation for Clustering via Prototype Scattering and Positive Sampling

128 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.CVShow all

5 papers · 1 filter

cs.CV2023★ 111 cited

RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation

Xiangyu Zhao, Zengxin Qi, Sheng Wang +4

Medical image segmentation methods are generally designed as fully-supervised to guarantee model performance, which require a significant amount of expert annotated samples that ar…

cs.CV2022★ 33 cited

Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels

Yiming Lei, Haiping Zhu, Junping Zhang +1

The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, w…

cs.CV2022★ 17 cited

Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

Jiaqi Gao, Zhizhong Huang, Yiming Lei +4

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a la…

cs.CV2021★ 128 cited

Learning Representation for Clustering via Prototype Scattering and Positive Sampling

Zhizhong Huang, Jie Chen, Junping Zhang +1

Existing deep clustering methods rely on either contrastive or non-contrastive representation learning for downstream clustering task. Contrastive-based methods thanks to negative…

cs.CV2020★ 40 cited

Convolutional Ordinal Regression Forest for Image Ordinal Estimation

Haiping Zhu, Hongming Shan, Yuheng Zhang +5

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression…