7 citations · 18 across the 6 of their papers we have counts for
6 papers
PracticalDG: Perturbation Distillation on Vision-Language Models for Hybrid Domain Generalization
Zining Chen, Weiqiu Wang, Zhicheng Zhao +3
Domain Generalization (DG) aims to resolve distribution shifts between source and target domains, and current DG methods are default to the setting that data from source and target…
EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images
Yinsong Xu, Jiaqi Tang, Aidong Men +1
Medical image segmentation has immense clinical applicability but remains a challenge despite advancements in deep learning. The Segment Anything Model (SAM) exhibits potential in…
Delving into the Continuous Domain Adaptation
Yinsong Xu, Zhuqing Jiang, Aidong Men +2
Existing domain adaptation methods assume that domain discrepancies are caused by a few discrete attributes and variations, e.g., art, real, painting, quickdraw, etc. We argue that…
Bag of Tricks for Out-of-Distribution Generalization
Zining Chen, Weiqiu Wang, Zhicheng Zhao +2
Recently, out-of-distribution (OOD) generalization has attracted attention to the robustness and generalization ability of deep learning based models, and accordingly, many strateg…
Seeing your sleep stage: cross-modal distillation from EEG to infrared video
Jianan Han, Shaoxing Zhang, Aidong Men +4
It is inevitably crucial to classify sleep stage for the diagnosis of various diseases. However, existing automated diagnosis methods mostly adopt the "gold-standard" lectroencepha…
Split to Be Slim: An Overlooked Redundancy in Vanilla Convolution
Qiulin Zhang, Zhuqing Jiang, Qishuo Lu +4
Many effective solutions have been proposed to reduce the redundancy of models for inference acceleration. Nevertheless, common approaches mostly focus on eliminating less importan…