3 citations · 4 across the 5 of their papers we have counts for
5 papers
HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Keying Zhu +5
Prompt-free adaptation of the Segment Anything Model (SAM) has emerged as a promising paradigm for automatic medical image segmentation. Existing methods mainly focus on prompt gen…
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting
Taoyu Wu, Yiyi Miao, Zhuoxiao Li +5
Efficient three-dimensional reconstruction and real-time visualization are critical in surgical scenarios such as endoscopy. In recent years, 3D Gaussian Splatting (3DGS) has demon…
A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
Jinfeng Wang, Yanhao Huang, Sifan Song +3
EEG emotion recognition faces significant hurdles due to noise interference, signal nonstationarity, and the inherent complexity of brain activity which make accurately emotion cla…
Contrastive Learning Via Equivariant Representation
Sifan Song, Jinfeng Wang, Qiaochu Zhao +6
Invariant Contrastive Learning (ICL) methods have achieved impressive performance across various domains. However, the absence of latent space representation for distortion (augmen…
ProMISe: Promptable Medical Image Segmentation using SAM
Jinfeng Wang, Sifan Song, Xinkun Wang +4
With the proposal of the Segment Anything Model (SAM), fine-tuning SAM for medical image segmentation (MIS) has become popular. However, due to the large size of the SAM model and…