Segmentation笔记4-Boundary-Aware Network for Fast and High-Accuracy Portrait Segmentation
创新点 1.提出了BANet 2.提出了refine loss 总结 图片引起不适。。。 网络流程如下： 图片经过语义分支得到1/4大小的特征图。将其上采样到原图大小，c = 1作为边界特征挖掘分支的边界注意力图。注意力图由BA loss监督。将原图与注意力图拼接，更有针对性的挖掘低水平的特征。最后在融合部分，首先将语义分支的特征图上采样原图大小，c = 1，与边界特征挖掘分支得到的特征融合获得更
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相关文章
- 1. Boundary-sensitive Network for Portrait Segmentation论文解读
- 2. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation
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- 4. RANet：Ranking Attention Network for Fast Video Object Segmentation
- 5. Automatic Portrait Segmentation for Image Stylization 翻译学习
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- 7. 【论文笔记】Learning Deconvolution Network for Semantic Segmentation
- 8. Dual Attention Network for Scene Segmentation 论文笔记
- 9. 论文：Fast Panoptic Segmentation Network
- 10. Learning Deconvolution Network for Semantic Segmentation 笔记