# Recurrent Iterative Gating Networks for Semantic Segmentation

Source: [Recurrent Iterative Gating Networks for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa65141e209131cea080c1>)

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

Data updated: 2025-10-25T21:17:53.140Z

Authors: Rezaul Karim, Md Amirul Islam, Neil D. B. Bruce

Published: 2018-11-20T02:26:48.000Z

## Abstract

In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information in neural networks in a top-down manner, and different variants on the core structure are considered. The iterative nature of this mechanism allows for gating to spread in both spatial extent and feature space. This is revealed to be a powerful mechanism with broad compatibility with common existing networks. Analysis shows how gating interacts with different network characteristics, and we also show that more shallow networks with gating may be made to perform better than much deeper networks that do not include RIGNet modules.

## 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/1811.08043>)

[PDF](<https://arxiv.org/pdf/1811.08043.pdf>)