An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional
Networks for Semantic Segmentation

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

8 September 2017

M. Jorge Cardoso

Sebastien Ourselin

Papers citing "An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation"

7 / 7 papers shown

Title
Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs Pranjal Aggarwal Aman Madaan Yiming Yang Mausam LRM 33 38 0 19 May 2023
Effective semantic segmentation in Cataract Surgery: What matters most? Theodoros Pissas Claudio S. Ravasio L. Cruz Christos Bergeles 26 23 0 13 Aug 2021
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning Fernando Pérez-García Rachel Sparks Sébastien Ourselin MedIm LM&MA 147 427 0 09 Mar 2020
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks Wenguan Wang Xiankai Lu Jianbing Shen David J. Crandall Ling Shao VOS 18 272 0 19 Jan 2020
Distributionally Robust Deep Learning using Hardness Weighted Sampling Lucas Fidon Michael Aertsen Thomas Deprest Doaa Emam Frédéric Guffens ... Andrew Melbourne Sébastien Ourselin Jan Deprest Georg Langs Tom Kamiel Magda Vercauteren OOD 22 10 0 08 Jan 2020
Box-driven Class-wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation Chunfeng Song Yan Huang Wanli Ouyang Liang Wang 19 216 0 26 Apr 2019
A two-stage 3D Unet framework for multi-class segmentation on full resolution image Chengjia Wang Tom J. MacGillivray G. Macnaught Guang Yang D. Newby SSeg SupR 18 73 0 12 Apr 2018