activity
20192022
most citedCUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor

46 citations · 48 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2022★ 1 cited

Exploring Generalizable Distillation for Efficient Medical Image Segmentation

Xingqun Qi, Zhuojie Wu, Min Ren +3

Efficient medical image segmentation aims to provide accurate pixel-wise predictions for medical images with a lightweight implementation framework. However, lightweight frameworks…

cs.CV2022★ 1 cited

ShowFace: Coordinated Face Inpainting with Memory-Disentangled Refinement Networks

Zhuojie Wu, Xingqun Qi, Zijian Wang +4

Face inpainting aims to complete the corrupted regions of the face images, which requires coordination between the completed areas and the non-corrupted areas. Recently, memory-ori…

eess.IV2021

CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation

Wenxuan Zou, Muyi Sun

In recent years, deep convolutional neural networks have made significant advances in pathology image segmentation. However, pathology image segmentation encounters with a dilemma…

eess.IV2021

PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation

Zhuojie Wu, Zijian Wang, Wenxuan Zou +4

3D to 2D retinal vessel segmentation is a challenging problem in Optical Coherence Tomography Angiography (OCTA) images. Accurate retinal vessel segmentation is important for the d…

cs.CV2019★ 46 cited

CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor

Xiaohui Zhao, Endi Niu, Zhuo Wu +1

Extracting key information from documents, such as receipts or invoices, and preserving the interested texts to structured data is crucial in the document-intensive streamline proc…