# Crowd Counting Using Scale-Aware Attention Networks

arXiv: https://arxiv.org/abs/1903.02025 · HTML: https://mlsanity.com/paper/1903.02025/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2019.

## Built on by (most cited first, 20 of 20)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning](https://mlsanity.com/paper/2308.10468/) (2023)
- [Training-free Object Counting with Prompts](https://mlsanity.com/paper/2307.00038/) (2023)
- [Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting](https://mlsanity.com/paper/2409.15953/) (2024)
- [The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network](https://mlsanity.com/paper/2207.07858/) (2022)
- [A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches](https://mlsanity.com/paper/2501.19184/) (2025)
- [Crowd counting with crowd attention convolutional neural network](https://mlsanity.com/paper/2204.07347/) (2022)
- [Hybrid attention network based on progressive embedding scale-context for crowd counting](https://mlsanity.com/paper/2106.02324/) (2021)
- [Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting](https://mlsanity.com/paper/2605.02752/) (2026)
- [Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background](https://mlsanity.com/paper/2101.01479/) (2021)
- [Multi-Level Attentive Convoluntional Neural Network for Crowd Counting](https://mlsanity.com/paper/2105.11422/) (2021)
- [Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting](https://mlsanity.com/paper/2106.12163/) (2021)
- [BBA-net: A bi-branch attention network for crowd counting](https://mlsanity.com/paper/2201.08983/) (2022)
- [Deep Learning Techniques for Visual Counting](https://mlsanity.com/paper/2206.03033/) (2022)
- [Multi-scale Feature Aggregation for Crowd Counting](https://mlsanity.com/paper/2208.05256/) (2022)
- [Crowd counting with segmentation attention convolutional neural network](https://mlsanity.com/paper/2204.07380/) (2022)
- [Inception-Based Crowd Counting -- Being Fast while Remaining Accurate](https://mlsanity.com/paper/2210.09796/) (2022)
- [HDNet: A Hierarchically Decoupled Network for Crowd Counting](https://mlsanity.com/paper/2212.05722/) (2022)
- [FGA: Fourier-Guided Attention Network for Crowd Count Estimation](https://mlsanity.com/paper/2407.06110/) (2024)
- [A Transformer-based Multimodal Fusion Model for Efficient Crowd Counting Using Visual and Wireless Signals](https://mlsanity.com/paper/2504.20178/) (2025)

## Abstract (arXiv)

In this paper, we consider the problem of crowd counting in images. Given an image of a crowded scene, our goal is to estimate the density map of this image, where each pixel value in the density map corresponds to the crowd density at the corresponding location in the image. Given the estimated density map, the final crowd count can be obtained by summing over all values in the density map. One challenge of crowd counting is the scale variation in images. In this work, we propose a novel scale-aware attention network to address this challenge. Using the attention mechanism popular in recent deep learning architectures, our model can automatically focus on certain global and local scales appropriate for the image. By combining these global and local scale attention, our model outperforms other state-of-the-art methods for crowd counting on several benchmark datasets.