# Label Refinement Network for Coarse-to-Fine Semantic Segmentation

Source: [Label Refinement Network for Coarse-to-Fine Semantic Segmentation](<https://www.sota2.com/research/paper/68fa63331e209131ce9fd56c>)

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

Data updated: 2025-10-25T20:35:17.063Z

Authors: Md Amirul Islam, Shujon Naha, Mrigank Rochan, Neil Bruce, Yang Wang

Published: 2017-03-01T23:42:30.000Z

## Abstract

We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions. The segmentation labels at a coarse resolution are used together with convolutional features to obtain finer resolution segmentation labels. We define loss functions at several stages in the network to provide supervisions at different stages. Our experimental results on several standard datasets demonstrate that the proposed model provides an effective way of producing pixel-wise dense image labeling.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1703.00551>)

[PDF](<https://arxiv.org/pdf/1703.00551.pdf>)