# Learning Dilation Factors for Semantic Segmentation of Street Scenes

Source: [Learning Dilation Factors for Semantic Segmentation of Street Scenes](<https://www.sota2.com/research/paper/68fa639d1e209131ce9ffad1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:45:17.380Z

Authors: Yang He, Margret Keuper, Bernt Schiele, Mario Fritz

Published: 2017-09-06T18:19:10.000Z

## Abstract

Contextual information is crucial for semantic segmentation. However, finding the optimal trade-off between keeping desired fine details and at the same time providing sufficiently large receptive fields is non trivial. This is even more so, when objects or classes present in an image significantly vary in size. Dilated convolutions have proven valuable for semantic segmentation, because they allow to increase the size of the receptive field without sacrificing image resolution. However, in current state-of-the-art methods, dilation parameters are hand-tuned and fixed. In this paper, we present an approach for learning dilation parameters adaptively per channel, consistently improving semantic segmentation results on street-scene datasets like Cityscapes and Camvid.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1709.01956>)

[PDF](<https://arxiv.org/pdf/1709.01956.pdf>)