  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.
