Search Results for author:
Found 4 papers, 2 papers with code
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation.
Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters
In this paper, we propose a novel neural network framework, called Multi-Rater Prism (MrPrism) to learn the medical image segmentation from multiple labels.
An Efficient Person Clustering Algorithm for Open Checkout-free Groceries
Then, to ensure that the method adapts to the dynamic and unseen person flow, we propose Graph Convolutional Network (GCN) with a simple Nearest Neighbor (NN) strategy to accurately cluster the instances of CSG.
Distilled Siamese Networks for Visual Tracking
This model is intuitively inspired by the one teacher vs. multiple students learning method typically employed in schools.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
