# Multi-Scale Attention Network for Crowd Counting

arXiv: https://arxiv.org/abs/1901.06026 · HTML: https://mlsanity.com/paper/1901.06026/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Crowd Counting by Self-supervised Transfer Colorization Learning and Global Prior Classification](https://mlsanity.com/paper/2105.09684/) (2021)
- [EHNet: An Efficient Hybrid Network for Crowd Counting and Localization](https://mlsanity.com/paper/2503.12061/) (2025)

## Abstract (arXiv)

In crowd counting datasets, people appear at different scales, depending on their distance from the camera. To address this issue, we propose a novel multi-branch scale-aware attention network that exploits the hierarchical structure of convolutional neural networks and generates, in a single forward pass, multi-scale density predictions from different layers of the architecture. To aggregate these maps into our final prediction, we present a new soft attention mechanism that learns a set of gating masks. Furthermore, we introduce a scale-aware loss function to regularize the training of different branches and guide them to specialize on a particular scale. As this new training requires annotations for the size of each head, we also propose a simple, yet effective technique to estimate them automatically. Finally, we present an ablation study on each of these components and compare our approach against the literature on 4 crowd counting datasets: UCF-QNRF, ShanghaiTech A & B and UCF_CC_50. Our approach achieves state-of-the-art on all them with a remarkable improvement on UCF-QNRF (+25% reduction in error).