57 citations · 88 across the 7 of their papers we have counts for
9 papers · 1 filter
Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission
Jun Wang, Sixian Wang, Jincheng Dai +3
As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learni…
Dispensed Transformer Network for Unsupervised Domain Adaptation
Yunxiang Li, Jingxiong Li, Ruilong Dan +10
Accurate segmentation is a crucial step in medical image analysis and applying supervised machine learning to segment the organs or lesions has been substantiated effective. Howeve…
GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation
Yunxiang Li, Shuai Wang, Jun Wang +5
To achieve an accurate assessment of root canal therapy, a fundamental step is to perform tooth root segmentation on oral X-ray images, in that the position of tooth root boundary…
CT-Net: Channel Tensorization Network for Video Classification
Kunchang Li, Xianhang Li, Yali Wang +2
3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions…
AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy
Yunxiang Li, Guodong Zeng, Yifan Zhang +10
Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results wi…
Two-phase weakly supervised object detection with pseudo ground truth mining
Jun Wang
Weakly Supervised Object Detection (WSOD), aiming to train detectors with only image-level dataset, has arisen increasing attention for researchers. In this project, we focus on tw…