Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR
Image Road Extraction

Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR Image Road Extraction

10 January 2022

Tao Chen

Papers citing "Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR Image Road Extraction"

5 / 5 papers shown

Title
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao ViT 505 3,709 0 24 Feb 2021
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping Kira Vinogradova Alexandr Dibrov E. Myers FAtt 102 149 0 26 Feb 2020
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics Alex Kendall Y. Gal R. Cipolla 3DH 268 3,122 0 19 May 2017
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen George Papandreou Iasonas Kokkinos Kevin Patrick Murphy Alan Yuille SSeg 235 18,224 0 02 Jun 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 1.0K 15,795 0 02 Nov 2015