5 citations · 18 across the 10 of their papers we have counts for
10 papers
Learning Brain Tumor Representation in 3D High-Resolution MR Images via Interpretable State Space Models
Qingqiao Hu, Daoan Zhang, Jiebo Luo +4
Learning meaningful and interpretable representations from high-dimensional volumetric magnetic resonance (MR) images is essential for advancing personalized medicine. While Vision…
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data
Daoan Zhang, Yunhao Luo, Jianguo Zhang
We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We first figure out th…
Cross Contrasting Feature Perturbation for Domain Generalization
Chenming Li, Daoan Zhang, Wenjian Huang +1
Domain generalization (DG) aims to learn a robust model from source domains that generalize well on unseen target domains. Recent studies focus on generating novel domain samples o…
Towards Generalizable Medical Image Segmentation with Pixel-wise Uncertainty Estimation
Shuai Wang, Zipei Yan, Daoan Zhang +4
Deep neural networks (DNNs) achieve promising performance in visual recognition under the independent and identically distributed (IID) hypothesis. In contrast, the IID hypothesis…
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
Shuai Wang, Daoan Zhang, Zipei Yan +2
Source-free domain adaptation aims to adapt deep neural networks using only pre-trained source models and target data. However, accessing the source model still has a potential con…
Prototype Knowledge Distillation for Medical Segmentation with Missing Modality
Shuai Wang, Zipei Yan, Daoan Zhang +3
Multi-modality medical imaging is crucial in clinical treatment as it can provide complementary information for medical image segmentation. However, collecting multi-modal data in…
Bootstrap The Original Latent: Learning a Private Model from a Black-box Model
Shuai Wang, Daoan Zhang, Jianguo Zhang +2
In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users t…
Feature Alignment and Uniformity for Test Time Adaptation
Shuai Wang, Daoan Zhang, Zipei Yan +2
Test time adaptation (TTA) aims to adapt deep neural networks when receiving out of distribution test domain samples. In this setting, the model can only access online unlabeled te…
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization
Daoan Zhang, Mingkai Chen, Chenming Li +2
Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generati…
Rethinking Alignment and Uniformity in Unsupervised Semantic Segmentation
Daoan Zhang, Chenming Li, Haoquan Li +3
Unsupervised image semantic segmentation(UISS) aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the…