# Multi Receptive Field Network for Semantic Segmentation

arXiv: https://arxiv.org/abs/2011.08577 · HTML: https://mlsanity.com/paper/2011.08577/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 11 of 11)

- [A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation](https://mlsanity.com/paper/2104.07256/) (2021)
- [Contrastive Boundary Learning for Point Cloud Segmentation](https://mlsanity.com/paper/2203.05272/) (2022)
- [ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts](https://mlsanity.com/paper/2301.12276/) (2023)
- [FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2208.14143/) (2022)
- [Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation](https://mlsanity.com/paper/2208.11499/) (2022)
- [Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries](https://mlsanity.com/paper/2110.02833/) (2021)
- [BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis](https://mlsanity.com/paper/2503.12539/) (2025)
- [DWRSeg: Rethinking Efficient Acquisition of Multi-scale Contextual Information for Real-time Semantic Segmentation](https://mlsanity.com/paper/2212.01173/) (2022)
- [L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter](https://mlsanity.com/paper/2211.11242/) (2022)
- [Spherical Feature Pyramid Networks For Semantic Segmentation](https://mlsanity.com/paper/2307.02658/) (2023)
- [An Empirical Study of Attention Networks for Semantic Segmentation](https://mlsanity.com/paper/2309.10217/) (2023)

## Abstract (arXiv)

Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image. Despite significant progress achieved recently, most existing methods still suffer from two challenging issues: 1) the size of objects and stuff in an image can be very diverse, demanding for incorporating multi-scale features into the fully convolutional networks (FCNs); 2) the pixels close to or at the boundaries of object/stuff are hard to classify due to the intrinsic weakness of convolutional networks. To address the first issue, we propose a new Multi-Receptive Field Module (MRFM), explicitly taking multi-scale features into account. For the second issue, we design an edge-aware loss which is effective in distinguishing the boundaries of object/stuff. With these two designs, our Multi Receptive Field Network achieves new state-of-the-art results on two widely-used semantic segmentation benchmark datasets. Specifically, we achieve a mean IoU of 83.0 on the Cityscapes dataset and 88.4 mean IoU on the Pascal VOC2012 dataset.