论文标题
半衰期为语义细分，并不确定性引导自我交叉监督
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
论文作者
论文摘要
作为实现半监督分割的强大方法，交叉监督方法使用丰富的未标记图像基于独立的集合模型来学习交叉一致性。但是，交叉监督产生的错误的伪标记信息会使训练过程感到困惑，并对分割模型的有效性产生负面影响。此外，在这种方法中，集合模型的培训过程还乘以计算资源的成本并降低培训效率。为了解决这些问题，我们提出了一种新颖的交叉监督方法，即不确定性引导的自我交叉监督（USCS）。除了合奏模型外，我们首先设计了一个多输入多输出（MIMO）细分模型，该模型可以通过共享模型生成多个输出，因此对输出施加了一致性，从而节省了参数和计算的成本。另一方面，我们采用不确定性作为指导信息来鼓励模型专注于伪标签的高自信区域，并减轻错误的伪标记在自交叉监督中的影响，从而提高细分模型的性能。广泛的实验表明，我们的方法可实现最先进的性能，同时节省40.5％和49.1％的参数和计算成本。
As a powerful way of realizing semi-supervised segmentation, the cross supervision method learns cross consistency based on independent ensemble models using abundant unlabeled images. However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model. Besides, the training process of ensemble models in such methods also multiplies the cost of computation resources and decreases the training efficiency. To solve these problems, we propose a novel cross supervision method, namely uncertainty-guided self cross supervision (USCS). In addition to ensemble models, we first design a multi-input multi-output (MIMO) segmentation model which can generate multiple outputs with shared model and consequently impose consistency over the outputs, saving the cost on parameters and calculations. On the other hand, we employ uncertainty as guided information to encourage the model to focus on the high confident regions of pseudo labels and mitigate the effects of wrong pseudo labeling in self cross supervision, improving the performance of the segmentation model. Extensive experiments show that our method achieves state-of-the-art performance while saving 40.5% and 49.1% cost on parameters and calculations.
