In semantic segmentation, training data down-sampling is commonly performed because of limited resources, adapting image size to the model input, or improving 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 pixels and labels. Hence, training performance significantly decreases as the down-sampling factor increases. In this paper, we bring together the downsampling strategies for the image data and annotated labels. To that aim, we propose a soft-labeling method for label down-sampling that takes advantage of structural content prior to down-sampling. Thereby, fully aligning softlabels with image data to keep the distribution of the sampled pixels. This proposal also produces richer annotations for under-represented semantic classes. Altogether, it permits training competitive models at lower resolutions. Experiments show that the proposal outperforms other downsampling strategies. Moreover, state of the art performance is achieved for reference benchmarks, but employing significantly less computational resources than other 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、适应模型输入图像大小或改进数据增强，通常会对训练数据进行下采样。这种下采样通常对图像数据和标注标签采用不同的策略，导致下采样像素与标签之间出现不匹配。因此，随着下采样因子的增加，训练性能显著下降。本文统一了图像数据和标注标签的下采样策略。为此，我们提出了一种用于标签下采样的软标注方法，该方法在下采样前利用结构先验信息，从而使软标注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更丰富的标注。总体而言，它允许在较低分辨率下训练具有竞争力的模型。实验表明，该方案优于其他下采样策略。此外，在参考基准测试中达到了最先进的性能，但所需计算资源远少于其他方法。该方案使得在资源受限条件下进行有竞争力的语义分割研究成为可能。