- 论文标题：Image Segmentation Using Deep Learning:A Survey
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- 研究背景：scene understanding,medical image analysis, robotic perception, video surveillance, augmented reality, and image compression
- 方法和性质：
fully convolutional pixel-labeling networks,encoder-decoder architectures, multi-scale and pyramid based approaches, recurrent networks, visual attention models, and generativemodels in adversarial settings.
- Fully convolutional networks
- Convolutional models with graphical models
- Encoder-decoder based models
- Multi-scale and pyramid network based models5) R-CNN based models (for instance segmentation)6) Dilated convolutional models and DeepLab family7) Recurrent neural network based models8) Attention-based models
- Generative models and adversarial training
- Convolutional models with active contour models
- Other models
- 研究结果：
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- 主要讲各个方法细节，之后再了解
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