L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter

21 November 2022

Papers citing "L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter"

5 / 5 papers shown

Title
Context-Aware Mixup for Domain Adaptive Semantic Segmentation Qianyu Zhou Zhengyang Feng Qiqi Gu Jiangmiao Pang Guangliang Cheng Xuequan Lu Jianping Shi Lizhuang Ma 63 129 0 08 Aug 2021
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation C. L. P. Chen C. Qin C. Ouyang Zeju Li Shuo Wang Huaqi Qiu Liang Chen G. Tarroni Wenjia Bai Daniel Rueckert GAN MedIm 51 40 0 07 Aug 2021
Initialization and Regularization of Factorized Neural Layers M. Khodak Neil A. Tenenholtz Lester W. Mackey Nicolò Fusi 63 56 0 03 May 2021
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank Inigo Alonso Alberto Sabater David Ferstl Luis Montesano Ana C. Murillo SSL CLL 119 202 0 27 Apr 2021
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 429 15,595 0 02 Nov 2015