64 citations · 140 across the 13 of their papers we have counts for
4 papers · 1 filter
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…