activity
20192025
most citedGaussian Dynamic Convolution for Efficient Single-Image Segmentation

57 citations · 170 across the 22 of their papers we have counts for

collaborators

28 papers

cs.LG20255 cited

MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments

Honghui Du, Leandro Minku, Huiyu Zhou

Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data stream…

cs.CV20221 cited

A lightweight multi-scale context network for salient object detection in optical remote sensing images

Yuhan Lin, Han Sun, Ningzhong Liu +3

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for opt…

eess.IV20223 cited

Neuroplastic graph attention networks for nuclei segmentation in histopathology images

Yoav Alon, Huiyu Zhou

Modern histopathological image analysis relies on the segmentation of cell structures to derive quantitative metrics required in biomedical research and clinical diagnostics. State…

cs.CV20212 cited

Dispensed Transformer Network for Unsupervised Domain Adaptation

Yunxiang Li, Jingxiong Li, Ruilong Dan +10

Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. Howeve…

eess.IV20211 cited

Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images

Yibao Sun, Giussepi Lopez, Yaqi Wang +3

Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment. However, factors like vague boun…

cs.CV2021

Robust Ensembling Network for Unsupervised Domain Adaptation

Han Sun, Lei Lin, Ningzhong Liu +1

Recently, in order to address the unsupervised domain adaptation (UDA) problem, extensive studies have been proposed to achieve transferrable models. Among them, the most prevalent…