64 citations · 140 across the 13 of their papers we have counts for
8 papers · 1 filter
SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma
Zexi Huang, Lihua Guo, Xin Yang +1
Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. Accurate and automatic segmentation of organs at risk (OAR) of computed tomography (CT) images is clinically significan…
Self-supervised Representation Learning for Trip Recommendation
Qiang Gao, Wei Wang, Kunpeng Zhang +2
Trip recommendation is a significant and engaging location-based service that can help new tourists make more customized travel plans. It often attempts to suggest a sequence of po…
GlassNet: Label Decoupling-based Three-stream Neural Network for Robust Image Glass Detection
C. Zheng, D. Shi, X. Yan +5
Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects be…
Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?
Zhoubo Xu, Puqing Chen, Romain Raveaux +2
Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate gr…
Prior-Induced Information Alignment for Image Matting
Yuhao Liu, Jiake Xie, Yu Qiao +2
Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image. However, most existing deep learning-based methods still suffer from the c…
Vertical Federated Learning without Revealing Intersection Membership
Jiankai Sun, Xin Yang, Yuanshun Yao +4
Vertical Federated Learning (vFL) allows multiple parties that own different attributes (e.g. features and labels) of the same data entity (e.g. a person) to jointly train a model.…
Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang +5
Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the pri…
CPT: Efficient Deep Neural Network Training via Cyclic Precision
Yonggan Fu, Han Guo, Meng Li +4
Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy ef…