# Gated-Attention Feature-Fusion Based Framework for Poverty Prediction

arXiv: https://arxiv.org/abs/2411.19690 · HTML: https://mlsanity.com/paper/2411.19690/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 1 of 1)

- [A Novel Generator with Auxiliary Branch for Improving GAN Performance](https://mlsanity.com/paper/2112.14968/) (2021)

## Built on by (most cited first, 2 of 2)

- [Split-Fuse-Transport: Annotation-Free Saliency via Dual Clustering and Optimal Transport Alignment](https://mlsanity.com/paper/2510.17484/) (2025)
- [DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features](https://mlsanity.com/paper/2606.07756/) (2026)

## Abstract (arXiv)

This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become outdated. To address these issues, we propose a state-of-the-art Convolutional Neural Network (CNN) architecture, extending the ResNet50 model by incorporating a Gated-Attention Feature-Fusion Module (GAFM). Our architecture is designed to improve the model's ability to capture and combine both global and local features from satellite images, leading to more accurate poverty estimates. The model achieves a 75% R2 score, significantly outperforming existing leading methods in poverty mapping. This improvement is due to the model's capacity to focus on and refine the most relevant features, filtering out unnecessary data, which makes it a powerful tool for remote sensing and poverty estimation.