Spatial Sampling Network for Fast Scene Understanding

Davide Mazzini, Raimondo Schettini; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


We propose a network architecture to perform efficient scene understanding. This work presents three main novelties: the first is a module named Improved Guided Upsampling Module that can replace in toto the decoder part in common semantic segmentation networks. Our second contribution is the introduction of a new module based on spatial sampling to perform Instance Segmentation. It provides a very fast instance segmentation needing only a simple post-processing step at inference time. Finally, we propose a novel efficient network design that includes the new modules and test it against different datasets for outdoor scene understanding. To our knowledge, our network is one of the most efficient architectures for scene understanding published to date, furthermore being 8.6% more accurate than the fastest competitor on semantic segmentation and almost five times faster than the most efficient network for instance segmentation.

Related Material


[pdf]
[bibtex]
@InProceedings{Mazzini_2019_CVPR_Workshops,
author = {Mazzini, Davide and Schettini, Raimondo},
title = {Spatial Sampling Network for Fast Scene Understanding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}