most citedPraNet: Parallel Reverse Attention Network for Polyp Segmentation

111 citations · 192 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV20214 cited

Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection

Wenbo Zhang, Ge-Peng Ji, Zhuo Wang +2

RGB-D salient object detection (SOD) recently has attracted increasing research interest by benefiting conventional RGB SOD with extra depth information. However, existing RGB-D SO…

cs.CV202132 cited

Camouflaged Object Segmentation with Distraction Mining

Haiyang Mei, Ge-Peng Ji, Ziqi Wei +3

Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings, which has a wide range of valuable applications. The key cha…

cs.CV2020

Siamese Network for RGB-D Salient Object Detection and Beyond

Keren Fu, Deng-Ping Fan, Ge-Peng Ji +3

Existing RGB-D salient object detection (SOD) models usually treat RGB and depth as independent information and design separate networks for feature extraction from each. Such sche…

eess.IV2020111 cited

PraNet: Parallel Reverse Attention Network for Polyp Segmentation

Deng-Ping Fan, Ge-Peng Ji, Tao Zhou +4

Colonoscopy is an effective technique for detecting colorectal polyps, which are highly related to colorectal cancer. In clinical practice, segmenting polyps from colonoscopy image…

eess.IV202016 cited

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

Deng-Ping Fan, Tao Zhou, Ge-Peng Ji +5

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis. Automated detection of lung infections from computed tomo…

cs.CV202029 cited

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

Keren Fu, Deng-Ping Fan, Ge-Peng Ji +1

This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection. Existing models usually treat RGB and depth as i…