NP-SemiSeg：当神经过程遇上半监督语义分割
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
AI总结：
本文提出NP-SemiSeg，将神经过程引入半监督语义分割以实现高效不确定性量化，并在PASCAL VOC 2012和Cityscapes上验证了其有效性。
摘要：
半监督语义分割涉及在训练时为未标注图像分配像素级标签。这在许多无法在时间或成本上实现像素级标注的现实应用中非常有用。当前的半监督语义分割方法通过从模型输出的类别概率分布中为每个像素预测伪标签来工作。然而，如果预测的概率分布不正确，就会导致较差的分割结果，这在安全关键系统中可能产生连锁后果，例如医学图像或自动驾驶汽车。因此，理解模型不知道什么非常重要，这主要通过不确定性量化来实现。最近，神经过程（NPs）已在半监督图像分类中得到探索，并且它们已成为一种计算高效且有效的不确定性量化方法。在这项工作中，我们向前迈进一步，将NPs适配到半监督语义分割中，从而产生了一个名为NP-SemiSeg的新模型。我们在公共基准PASCAL VOC 2012和Cityscapes上，采用不同的训练设置对NP-SemiSeg进行了实验评估，结果验证了其有效性。
英文摘要：
Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasible in time or cost. Current approaches to semi-supervised semantic segmentation work by predicting pseudo-labels for each pixel from a class-wise probability distribution output by a model. If the predicted probability distribution is incorrect, however, this leads to poor segmentation results, which can have knock-on consequences in safety critical systems, like medical images or self-driving cars. It is, therefore, important to understand what a model does not know, which is mainly achieved by uncertainty quantification. Recently, neural processes (NPs) have been explored in semi-supervised image classification, and they have been a computationally efficient and effective method for uncertainty quantification. In this work, we move one step forward by adapting NPs to semi-supervised semantic segmentation, resulting in a new model called NP-SemiSeg. We experimentally evaluated NP-SemiSeg on the public benchmarks PASCAL VOC 2012 and Cityscapes, with different training settings, and the results verify its effectiveness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
