85 citations · 273 across the 17 of their papers we have counts for
13 papers · 1 filter
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Tianyang Hu, Jun Wang, Wenjia Wang +1
Deep learning has achieved many breakthroughs in modern classification tasks. Numerous architectures have been proposed for different data structures but when it comes to the loss…
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…
PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement
Jun Wang, Hefeng Zhou, Xiaohan Yu
Current state-of-the-art weakly supervised object detection (WSOD) studies mainly follow a two-stage training strategy which integrates a fully supervised detector (FSD) with a pur…
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
Jun Wang, Xiaohan Yu, Yongsheng Gao
The core for tackling the fine-grained visual categorization (FGVC) is to learn subtle yet discriminative features. Most previous works achieve this by explicitly selecting the dis…
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
Jun Wang, Yang Zhao, Linglong Qian +2
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e.g., diabetic, hypertensive and solar retinopathies. E…
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…
Self-Adaptive Transfer Learning for Multicenter Glaucoma Classification in Fundus Retina Images
Yiming Bao, Jun Wang, Tong Li +4
The early diagnosis and screening of glaucoma are important for patients to receive treatment in time and maintain eyesight. Nowadays, deep learning (DL) based models have been suc…
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…
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
Yunxiang Li, Yifan Zhang, Yaqi Wang +7
As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists. It is still challenging to seg…
Mask Guided Attention For Fine-Grained Patchy Image Classification
Jun Wang, Xiaohan Yu, Yongsheng Gao
In this work, we present a novel mask guided attention (MGA) method for fine-grained patchy image classification. The key challenge of fine-grained patchy image classification lies…
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Jingxiong Li, Yaqi Wang, Shuai Wang +4
Coronavirus disease 2019 (COVID-19) is one of the most destructive pandemic after millennium, forcing the world to tackle a health crisis. Automated lung infections classification…