然后利用分割预测网络(SP-Net)对I_0和S_0进行分割预测得到S_R。最后将S_R传递到分割引导网络(SG-Net)作为输入,对最终结果进行预测。 3.1 Segmentation Prediction Network (SP-Net) 网络架构: SP-Net的目标是在缺失的洞中预测分割标签映射。SP-Net的生成器基于FCN,但用残块代替了膨胀卷积层。用渐进式扩张因子...
We propose a method SPGNet for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning. In this method, the 2D-to-3D-lifting network predicts the global position and coordinates of the 3D human pose. Then, we re-project the estimated 3D pose back to the...
Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only ‘fine’ annotations.Type Uncategorized Publication International Conference on Computer Vision Date October, 2019 ...
Our model factorizes the image inpainting process into segmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two steps, which predict the segmentation labels in the missing area first, and then generate segmentation guided inpainting results. Experiments on multiple public datasets ...
git clone https://github.com/cszy98/SPGNet.git. cd SPGNet Prerequisites - python3.7 - pytorch1.2.0 + torchvision0.4.0 - numpy - opencv - tqdm Create environment and install dependencies: # 1. Create a conda virtual environment. conda create -n spgnet python=3.7 anaconda source activate...
@@ -31,7 +31,7 @@ conda create -n spgnet python=3.7 anaconda source activate spgnet # 2. Install dependency pip install -r requirement.txt pip install -r requirements.txt ``` ### Data Preparation @@ -84,4 +84,4 @@ If you find our work useful in your research or publication,...
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