# Mean Field inference of CRFs based on GAT

Source: [Mean Field inference of CRFs based on GAT](<https://www.sota2.com/research/paper/68fa6ca21e209131cea35150>)

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

Data updated: 2025-10-26T00:14:46.163Z

Authors: LingHong Xing, XiangXiang Ma, GuangSheng Luo

Published: 2022-05-29T05:47:56.000Z

## Abstract

In this paper we propose an improved mean-field inference algorithm for the fully connected paired CRFs model. The improved method Message Passing operation is changed from the original linear convolution to the present graph attention operation, while the process of the inference algorithm is turned into the forward process of the GAT model. Combined with the mean-field inferred label distribution, it is equivalent to the output of a classifier with only unary potential. To this end, we propose a graph attention network model with residual structure, and the model approach is applicable to all sequence annotation tasks, such as pixel-level image semantic segmentation tasks as well as text annotation tasks.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.15312>)

[PDF](<https://arxiv.org/pdf/2205.15312.pdf>)