In semantic segmentation, training data down-sampling is commonly performed due to limited resources, the need to adapt image size to the model input, or improve data augmentation. This down-sampling typically employs different strategies for the image data and the annotated labels. Such discrepancy leads to mismatches between the down-sampled color and label images. Hence, the training performance significantly decreases as the down-sampling factor increases. In this paper, we bring together the down-sampling strategies for the image data and the training labels. To that aim, we propose a novel framework for label down-sampling via soft-labeling that better conserves label information after down-sampling. Therefore, fully aligning soft-labels with image data to keep the distribution of the sampled pixels. This proposal also produces reliable annotations for under-represented semantic classes. Altogether, it allows training competitive models at lower resolutions. Experiments show that the proposal outperforms other down-sampling strategies. Moreover, state-of-the-art performance is achieved for reference benchmarks, but employing significantly less computational resources than foremost approaches. This proposal enables competitive research for semantic segmentation under resource constraints.

翻译：在语义分割中，由于资源限制、需要使图像尺寸适配模型输入或改进数据增强，通常会对训练数据进行降采样。这种降采样通常对图像数据和标注标签采用不同策略，导致降采样后的彩色图像与标签图像之间存在不匹配。因此，随着降采样因子增大，训练性能显著下降。本文统一了图像数据与训练标签的降采样策略。为此，我们提出了一种基于软标签的新型标签降采样框架，该框架能更好地保留降采样后的标签信息，从而使软标签与图像数据完全对齐以保持采样像素的分布。该方案还能为代表性不足的语义类别生成可靠标注。总体而言，它使得在较低分辨率下训练具有竞争力的模型成为可能。实验表明，该方案优于其他降采样策略。此外，在参考基准测试中，我们以显著低于主流方法的计算资源达到了最先进的性能。该方案使得在资源受限条件下开展语义分割的竞争性研究成为可能。