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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

Tao Chen
Yiran Liu
Haoyu Jiang
Ruirui Li
    ViT
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Papers citing "Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR Image Road Extraction"

4 / 4 papers shown
Title
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
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
499
3,709
0
24 Feb 2021
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
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
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall
Y. Gal
R. Cipolla
3DH
262
3,122
0
19 May 2017
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan
Alex Kendall
R. Cipolla
SSeg
1.0K
15,768
0
02 Nov 2015

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