# Semantic Segmentation With Multi Scale Spatial Attention For Self Driving Cars

arXiv: https://arxiv.org/abs/2007.12685 · HTML: https://mlsanity.com/paper/2007.12685/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [DMSANet: Dual Multi Scale Attention Network](https://mlsanity.com/paper/2106.08382/) (2021)
- [ViTBIS: Vision Transformer for Biomedical Image Segmentation](https://mlsanity.com/paper/2201.05920/) (2022)
- [PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation](https://mlsanity.com/paper/2305.15709/) (2023)
- [AA3DNet: Attention Augmented Real Time 3D Object Detection](https://mlsanity.com/paper/2107.12137/) (2021)
- [AASeg: Attention Aware Network for Real Time Semantic Segmentation](https://mlsanity.com/paper/2108.04349/) (2021)
- [Exploiting Richness of Learned Compressed Representation of Images for Semantic Segmentation](https://mlsanity.com/paper/2307.01524/) (2023)

## Abstract (arXiv)

In this paper, we present a novel neural network using multi scale feature fusion at various scales for accurate and efficient semantic image segmentation. We used ResNet based feature extractor, dilated convolutional layers in downsampling part, atrous convolutional layers in the upsampling part and used concat operation to merge them. A new attention module is proposed to encode more contextual information and enhance the receptive field of the network. We present an in depth theoretical analysis of our network with training and optimization details. Our network was trained and tested on the Camvid dataset and Cityscapes dataset using mean accuracy per class and Intersection Over Union (IOU) as the evaluation metrics. Our model outperforms previous state of the art methods on semantic segmentation achieving mean IOU value of 74.12 while running at >100 FPS.