44 citations · 97 across the 9 of their papers we have counts for
11 papers
DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization
Feng Hou, Jin Yuan, Ying Yang +7
Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-langu…
Learning to Learn Domain-invariant Parameters for Domain Generalization
Feng Hou, Yao Zhang, Yang Liu +6
Due to domain shift, deep neural networks (DNNs) usually fail to generalize well on unknown test data in practice. Domain generalization (DG) aims to overcome this issue by capturi…
Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective
Lanning Wei, Huan Zhao, Zhiqiang He
In recent years, Graph Neural Networks (GNNs) have shown superior performance on diverse real-world applications. To improve the model capacity, besides designing aggregation opera…
Pooling Architecture Search for Graph Classification
Lanning Wei, Huan Zhao, Quanming Yao +1
Graph classification is an important problem with applications across many domains, like chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of-the…
TumorCP: A Simple but Effective Object-Level Data Augmentation for Tumor Segmentation
Jiawei Yang, Yao Zhang, Yuan Liang +3
Deep learning models are notoriously data-hungry. Thus, there is an urging need for data-efficient techniques in medical image analysis, where well-annotated data are costly and ti…
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
Yao Zhang, Jiawei Yang, Jiang Tian +4
Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for t…