# Efficient semantic image segmentation with superpixel pooling

arXiv: https://arxiv.org/abs/1806.02705 · HTML: https://mlsanity.com/paper/1806.02705/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 3 of 3)

- [DACoN: DINO for Anime Paint Bucket Colorization with Any Number of Reference Images](https://mlsanity.com/paper/2509.14685/) (2025)
- [PeCA: Palette Context Assisted Inference for Test-Time Paint-Bucket Colourisation on Animation Videos](https://mlsanity.com/paper/2608.00903/) (2026)
- [Region Aware Squeeze-and-Excitation Networks for Image Segmentation](https://mlsanity.com/paper/2610.15079/) (2026)

## Abstract (arXiv)

In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at https://github.com/bermanmaxim/superpixPool