Search Results for author:
Found 3 papers, 1 papers with code
LACFormer: Toward accurate and efficient polyp segmentation
The proposed model combines the strengths of Transformers and CNNs along with Laplacian images to overcome the limitations of previous models.
LAPFormer: A Light and Accurate Polyp Segmentation Transformer
Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities.
Modular Resource Centric Learning for Workflow Performance Prediction
The central idea of this work is to train resource-centric machine learning agents to capture complex relationships between a set of program instructions and their performance metrics when executed on a specific resource.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
