论文标题
用于改进语义分割的合成卷积特征
Synthetic Convolutional Features for Improved Semantic Segmentation
论文作者
论文摘要
最近，基于学习的图像合成使得能够生成高分辨率的图像，即应用流行的对抗训练或强大的感知损失。但是，成功利用合成数据来通过其他合成图像改善语义分割仍然具有挑战性。因此，我们建议生成中间的卷积特征，并提出第一种符合此类中间卷积特征的合成方法。这使我们能够从标签面具中生成新功能，并将它们成功地包括在训练过程中，以提高语义细分的性能。对两个具有挑战性的数据集和ADE20K的实验结果和分析表明，我们生成的功能改善了细分任务的性能。
Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss. However, it remains challenging to successfully leverage synthetic data for improving semantic segmentation with additional synthetic images. Therefore, we suggest to generate intermediate convolutional features and propose the first synthesis approach that is catered to such intermediate convolutional features. This allows us to generate new features from label masks and include them successfully into the training procedure in order to improve the performance of semantic segmentation. Experimental results and analysis on two challenging datasets Cityscapes and ADE20K show that our generated feature improves performance on segmentation tasks.
