Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the uncertainty in the pixel-labeling process are not completely addressed. As such, we present a new approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process. The pixel-wise weights are used during training to increase or decrease the importance of the pixels. Experimental results show that the proposed approach leads to significant improvements in three challenging segmentation tasks in comparison to baseline methods. It was also proved to be more invariant to noise. The approach presented here may be used within a wide range of semantic segmentation methods to improve their robustness.

翻译：最近,基于革命神经网络(CNN)的方法在语义分割任务方面取得了令人瞩目的成功,然而,诸如阶级不平衡和像素标签过程中的不确定性等挑战没有完全得到解决。因此,我们提出了一个新方法,根据标签过程中的等级和不确定性,计算每个像素的重量。在培训过程中使用了像素偏重来增加或降低像素的重要性。实验结果显示,与基线方法相比,拟议的方法在三种具有挑战性的分割任务上取得了显著的改进。这也证明它对于噪音来说更具易变性。这里介绍的方法可以在广泛的语义分割方法中使用,以提高其稳健性。