# Linear Attention Mechanism: An Efficient Attention for Semantic Segmentation

Source: [Linear Attention Mechanism: An Efficient Attention for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa682b1e209131cea1a77a>)

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

Data updated: 2025-10-25T22:30:00.158Z

Authors: Rui Li, Jianlin Su, Chenxi Duan, Shunyi Zheng

Published: 2020-07-29T15:18:46.000Z

## Abstract

In this paper, to remedy this deficiency, we propose a Linear Attention Mechanism which is approximate to dot-product attention with much less memory and computational costs. The efficient design makes the incorporation between attention mechanisms and neural networks more flexible and versatile. Experiments conducted on semantic segmentation demonstrated the effectiveness of linear attention mechanism. Code is available at https://github.com/lironui/Linear-Attention-Mechanism.

## 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/2007.14902>)

[PDF](<https://arxiv.org/pdf/2007.14902.pdf>)