图像分割“The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation”
code:: https://github.com/SimJeg/FC-DenseNet DSOD中的一篇参考文献，不需要预训练进行图像分割，同样使用了DenseNet。在CamVid和Gatech数据库上，没有预训练，没有CRF后处理，达到了state-of-the-art的效果。 DenseNet的优点：（1）参数有效性，参数使用效率高；（2）隐式深层监督，short paths；（3）特征重
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相关文章
- 1. 【图像分割论文阅读】The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- 2. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
- 3. The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation_2016
- 4. 语义分割--Fully Convolutional DenseNets for Semantic Segmentation
- 5. The One Hundred Layers Tiramisu：Fully Convolutional DenseNets for Semantic Segmentation手动翻译
- 6. 图像分割“Fully Convolutional Instance-aware Semantic Segmentation”
- 7. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 8. FCN-Fully Convolutional Networks for Semantic Segmentation
- 9. Fully Convolutional Networks for Semantic Segmentation
- 10. 深度图像分割 -- Fully Convolutional Networks for Semantic Segmentation