64 citations · 140 across the 13 of their papers we have counts for
14 papers
CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction
Gangwei Xu, Huan Zhou, Xin Yang
In this paper, we propose CGI-Stereo, a novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization abili…
Bidirectional Semi-supervised Dual-branch CNN for Robust 3D Reconstruction of Stereo Endoscopic Images via Adaptive Cross and Parallel Supervisions
Hongkuan Shi, Zhiwei Wang, Ying Zhou +3
Semi-supervised learning via teacher-student network can train a model effectively on a few labeled samples. It enables a student model to distill knowledge from the teacher's pred…
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
Jeffry Wicaksana, Zengqiang Yan, Dong Zhang +4
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image…
Attention Concatenation Volume for Accurate and Efficient Stereo Matching
Gangwei Xu, Junda Cheng, Peng Guo +1
Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of hig…
CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation
Yu Qiao, Jincheng Zhu, Chengjiang Long +4
Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotat…
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…
TENet: Triple Excitation Network for Video Salient Object Detection
Sucheng Ren, Chu Han, Xin Yang +2
In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, s…