61 citations · 337 across the 72 of their papers we have counts for
14 papers · 2 filters
Exploring Flat Minima for Domain Generalization with Large Learning Rates
Jian Zhang, Lei Qi, Yinghuan Shi +1
Domain Generalization (DG) aims to generalize to arbitrary unseen domains. A promising approach to improve model generalization in DG is the identification of flat minima. One typi…
Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning
Guan Gui, Zhen Zhao, Lei Qi +3
In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong aug…
IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilization
Zekun Li, Lei Qi, Yinghuan Shi +1
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled…
DomainAdaptor: A Novel Approach to Test-time Adaptation
Jian Zhang, Lei Qi, Yinghuan Shi +1
To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificit…
DomainDrop: Suppressing Domain-Sensitive Channels for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi
Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance de…
Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization
Xiran Wang, Jian Zhang, Lei Qi +1
Domain generalization (DG) is proposed to deal with the issue of domain shift, which occurs when statistical differences exist between source and target domains. However, most curr…
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Lihe Yang, Zhen Zhao, Lei Qi +3
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly…
A Novel Cross-Perturbation for Single Domain Generalization
Dongjia Zhao, Lei Qi, Xiao Shi +2
Single domain generalization aims to enhance the ability of the model to generalize to unknown domains when trained on a single source domain. However, the limited diversity in the…
3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks
Heng Cai, Lei Qi, Qian Yu +2
Medical image segmentation typically necessitates a large and precisely annotated dataset. However, obtaining pixel-wise annotation is a labor-intensive task that requires signific…
NormAUG: Normalization-guided Augmentation for Domain Generalization
Lei Qi, Hongpeng Yang, Yinghuan Shi +1
Deep learning has made significant advancements in supervised learning. However, models trained in this setting often face challenges due to domain shift between training and test…
Generalizable Metric Network for Cross-domain Person Re-identification
Lei Qi, Ziang Liu, Yinghuan Shi +1
Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain genera…
Patch-aware Batch Normalization for Improving Cross-domain Robustness
Lei Qi, Dongjia Zhao, Yinghuan Shi +1
Despite the significant success of deep learning in computer vision tasks, cross-domain tasks still present a challenge in which the model's performance will degrade when the train…
ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization
Jintao Guo, Na Wang, Lei Qi +1
Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are ba…
Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation
Heng Cai, Shumeng Li, Lei Qi +3
Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation. Compared with 2D images, 3D medical volumes i…