# Spatial Sampling Network for Fast Scene Understanding

## TL;DR

This paper introduces a highly efficient scene understanding network with novel modules for improved semantic and instance segmentation, achieving state-of-the-art speed and accuracy on outdoor datasets.

## Contribution

The paper presents a new network architecture with an Improved Guided Upsampling Module and a spatial sampling-based instance segmentation module, significantly enhancing efficiency and accuracy.

## Key findings

- 8.6% more accurate than the fastest competitor in semantic segmentation
- Almost five times faster for instance segmentation
- Achieves high efficiency in outdoor scene understanding

## Abstract

We propose a network architecture to perform efficient scene understanding. This work presents three main novelties: the first is an 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 thresholding as 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 themost 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.