Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions [article]

Kashyap Chitta, Jose M. Alvarez, Martial Hebert
2019 arXiv   pre-print
Semantic segmentation with Convolutional Neural Networks is a memory-intensive task due to the high spatial resolution of feature maps and output predictions. In this paper, we present Quadtree Generating Networks (QGNs), a novel approach able to drastically reduce the memory footprint of modern semantic segmentation networks. The key idea is to use quadtrees to represent the predictions and target segmentation masks instead of dense pixel grids. Our quadtree representation enables hierarchical
more » ... processing of an input image, with the most computationally demanding layers only being used at regions in the image containing boundaries between classes. In addition, given a trained model, our representation enables flexible inference schemes to trade-off accuracy and computational cost, allowing the network to adapt in constrained situations such as embedded devices. We demonstrate the benefits of our approach on the Cityscapes, SUN-RGBD and ADE20k datasets. On Cityscapes, we obtain an relative 3 similar memory consumption; and only receive a 3 to a large dilated network, while reducing memory consumption by over 4×.
arXiv:1907.11821v2 fatcat:absnoy5i5zeiva26nzjbgbpoju