2020 最新 深度学习 图像分割技术 串烧
- 具体技术包罗：
- 神经网络分类
- 模型
- **主流公开数据集**
- **主流评价指标**
- **模型得分汇总**
由于这两年深度学习图像分割技术出现大量有趣和前瞻的分割技术，现在这里做一篇回顾性的总结，以提升学习效率。涉及语义分割、实例分割等。
具体技术包罗：
1.卷积网络家族
2.编解码器家族
3.多尺度金字塔结构
4.R-CNN模型做实例分割
5.膨胀卷积和Deeplab系列
6.RNN
7.注意力机制
8.对抗网络GAN
神经网络分类
CNNs、
RNN (LSTM)
编解码器
Encoder-Decoder 和 Auto-Encoder模型
对抗网络
迁移学习
模型
FCN
Skip-connection
ParseNet
Encoder Decoder
SegNet
HRNet
U-Net
V-Net
Multi-layer perceptron (MLPs)
PSPN
R-CNN based models for Instance Segmentation
Mask R-CNN
MaskLab
Dilated Convolutional Models and Deeplab Family
DeepLab
DeepLab v3+
Attention-Based Models
Attention-based semantic segmentation model
The reverse attention network for segmentation
The dual attention netowrk for semantic segmentation
Generative Models and Adversarial Training
A semi-supervised segmentation framework
Adversarial network with Multi-scale L1 loss
EncNet
RefineNet
Seednet
Feedforward-Net
BoxSup
Graph-convolutional net
Wide ResNet
Exfuse
DIS dual image segmentation
FoveaNet
Ladder DenseNet
BiSeNet
SPGNet
Gated shape CNNs
AC-Net
DSSPN
SGR
SAC
UperNet
主流公开数据集
2D
1. PASCAL Visual Object Classed (VOC)
2. PASCAL context
3. Microsoft Common Objects in Context (MS COCO)
4. Cityscapes
5. ADE20K / MIT Scene Parsing (SceneParse150)
6. SiftFlow
7. Stanford background
8. Berkeley Segmentation Dataset (BSD)
9. Youtube-Ojects
10. KITTI
2.5D
11. NYU-D
12. SUN-3D
13. SUN RGB-D
14. UW RGB-D Object Dataset
15. ScanNet3D
16. Stanford 2D-3D
17. ShapeNet Core
18. Sydney Urban Objects Dataset主流评价指标
Pixel accuracy
Mean Pixel Accuracy (MPA)
IOU / Jaccard Index /Mean-IOU
Precision / Recall / F1
Dice
模型得分汇总
