# Vortex Pooling: Improving Context Representation in Semantic Segmentation

arXiv: https://arxiv.org/abs/1804.06242 · HTML: https://mlsanity.com/paper/1804.06242/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2018.

## Built on by (most cited first, 4 of 4)

- [A Real-Time Online Learning Framework for Joint 3D Reconstruction and Semantic Segmentation of Indoor Scenes](https://mlsanity.com/paper/2108.05246/) (2021)
- [LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN](https://mlsanity.com/paper/2201.09049/) (2022)
- [LTC-GIF: Attracting More Clicks on Feature-length Sports Videos](https://mlsanity.com/paper/2201.09077/) (2022)
- [Technical Report on Subspace Pyramid Fusion Network for Semantic Segmentation](https://mlsanity.com/paper/2204.01278/) (2022)

## Abstract (arXiv)

Semantic segmentation is a fundamental task in computer vision, which can be considered as a per-pixel classification problem. Recently, although fully convolutional neural network (FCN) based approaches have made remarkable progress in such task, aggregating local and contextual information in convolutional feature maps is still a challenging problem. In this paper, we argue that, when predicting the category of a given pixel, the regions close to the target are more important than those far from it. To tackle this problem, we then propose an effective yet efficient approach named Vortex Pooling to effectively utilize contextual information. Empirical studies are also provided to validate the effectiveness of the proposed method. To be specific, our approach outperforms the previous state-of-the-art model named DeepLab v3 by 1.5% on the PASCAL VOC 2012 val set and 0.6% on the test set by replacing the Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed Vortex Pooling. Moreover, our model (10.13FPS) shares similar computation cost with DeepLab v3 (10.37 FPS).