most citedM2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

16 citations · 21 across the 4 of their papers we have counts for

collaborators

5 papers

eess.IV202016 cited

M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients

Tao Zhou, Huazhu Fu, Yu Zhang +4

Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Although many OS time prediction methods have bee…

cs.CV2020

Learning Video Object Segmentation from Unlabeled Videos

Xiankai Lu, Wenguan Wang, Jianbing Shen +3

We propose a new method for video object segmentation (VOS) that addresses object pattern learning from unlabeled videos, unlike most existing methods which rely heavily on extensi…

cs.CV2020

Human-Aware Motion Deblurring

Ziyi Shen, Wenguan Wang, Xiankai Lu +4

This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG). The proposed model is based on a triple-bra…

cs.CV20203 cited

See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks

Xiankai Lu, Wenguan Wang, Chao Ma +3

We introduce a novel network, called CO-attention Siamese Network (COSNet), to address the unsupervised video object segmentation task from a holistic view. We emphasize the import…

cs.CV20202 cited

Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks

Wenguan Wang, Xiankai Lu, Jianbing Shen +2

This work proposes a novel attentive graph neural network (AGNN) for zero-shot video object segmentation (ZVOS). The suggested AGNN recasts this task as a process of iterative info…