most citedClass-Specific Distribution Alignment for Semi-Supervised Medical Image Classification

12 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20231 cited

FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

Qinghua Lin, Zuoyong Li, Kun Zeng +3

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process…

cs.CV202312 cited

Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification

Zhongzheng Huang, Jiawei Wu, Tao Wang +2

Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imb…

cs.CV20238 cited

Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment

Tao Wang, Zhongzheng Huang, Jiawei Wu +2

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due…

cs.CV20231 cited

dugMatting: Decomposed-Uncertainty-Guided Matting

Jiawei Wu, Changqing Zhang, Zuoyong Li +3

Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing. Due to the highly ill-posed issue, additional inputs, typic…

cs.CV2023

Spatio-Temporal Context Modeling for Road Obstacle Detection

Xiuen Wu, Tao Wang, Lingyu Liang +2

Road obstacle detection is an important problem for vehicle driving safety. In this paper, we aim to obtain robust road obstacle detection based on spatio-temporal context modeling…