Dense pixel-wise classification maps output by deep neural networks are of extreme importance for scene understanding. However, these maps are often partially inaccurate due to a variety of possible factors. Therefore, we propose to interactively refine them within a framework named DISCA (Deep Image Segmentation with Continual Adaptation). It consists of continually adapting a neural network to a target image using an interactive learning process with sparse user annotations as ground-truth. We show through experiments on three datasets using synthesized annotations the benefits of the approach, reaching an IoU improvement up to 4.7% for ten sampled clicks. Finally, we exhibit that our approach can be particularly rewarding when it is faced to additional issues such as domain adaptation.

中文翻译：

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地球观测中语义分割的交互式学习

深度神经网络输出的密集像素分类图对于场景理解极其重要。然而，由于各种可能的因素，这些地图往往部分不准确。因此，我们建议在名为 DISCA（具有持续适应的深度图像分割）的框架内交互式地改进它们。它包括使用交互式学习过程将神经网络不断适应目标图像，并将稀疏用户注释作为真实情况。我们通过使用合成注释的三个数据集的实验展示了该方法的好处，十次采样点击的 IoU 改进高达 4.7%。最后，我们展示了我们的方法在面临域适应等其他问题时特别有益。