  Recent progress of deep image classification models has provided great
potential to improve state-of-the-art performance in related computer vision
tasks. However, the transition to semantic segmentation is hampered by strict
memory limitations of contemporary GPUs. The extent of feature map caching
required by convolutional backprop poses significant challenges even for
moderately sized Pascal images, while requiring careful architectural
considerations when the source resolution is in the megapixel range. To address
these concerns, we propose a novel DenseNet-based ladder-style architecture
which features high modelling power and a very lean upsampling datapath. We
also propose to substantially reduce the extent of feature map caching by
exploiting inherent spatial efficiency of the DenseNet feature extractor. The
resulting models deliver high performance with fewer parameters than
competitive approaches, and allow training at megapixel resolution on commodity
hardware. The presented experimental results outperform the state-of-the-art in
terms of prediction accuracy and execution speed on Cityscapes, Pascal VOC
2012, CamVid and ROB 2018 datasets. Source code will be released upon
publication.
