# SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting

arXiv: https://arxiv.org/abs/1908.03716 · HTML: https://mlsanity.com/paper/1908.03716/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2019.

## Built on by (most cited first, 28 of 28)

- [An End-to-End Transformer Model for Crowd Localization](https://mlsanity.com/paper/2202.13065/) (2022)
- [Focal Inverse Distance Transform Maps for Crowd Localization](https://mlsanity.com/paper/2102.07925/) (2021)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images](https://mlsanity.com/paper/2401.10530/) (2024)
- [Dense Point Prediction: A Simple Baseline for Crowd Counting and Localization](https://mlsanity.com/paper/2104.12505/) (2021)
- [WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting](https://mlsanity.com/paper/2103.09408/) (2021)
- [Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation](https://mlsanity.com/paper/2206.10075/) (2022)
- [Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar](https://mlsanity.com/paper/2110.05876/) (2021)
- [Vehicle Perception from Satellite](https://mlsanity.com/paper/2402.00703/) (2024)
- [VMambaCC: A Visual State Space Model for Crowd Counting](https://mlsanity.com/paper/2405.03978/) (2024)
- [A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting](https://mlsanity.com/paper/2202.03843/) (2022)
- [Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting](https://mlsanity.com/paper/2108.08784/) (2021)
- [Progressive Multi-resolution Loss for Crowd Counting](https://mlsanity.com/paper/2212.04127/) (2022)
- [Hybrid attention network based on progressive embedding scale-context for crowd counting](https://mlsanity.com/paper/2106.02324/) (2021)
- [CAT: Learning to Collaborate Channel and Spatial Attention from Multi-Information Fusion](https://mlsanity.com/paper/2212.06335/) (2022)
- [Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting](https://mlsanity.com/paper/2106.12163/) (2021)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need](https://mlsanity.com/paper/2410.18368/) (2024)
- [SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New City](https://mlsanity.com/paper/2110.09452/) (2021)
- [Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches](https://mlsanity.com/paper/2204.04653/) (2022)
- [MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting](https://mlsanity.com/paper/2208.06761/) (2022)
- [A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting](https://mlsanity.com/paper/2401.05968/) (2024)
- [Hybrid Feature Collaborative Reconstruction Network for Few-Shot Fine-Grained Image Classification](https://mlsanity.com/paper/2407.02123/) (2024)
- [FGA: Fourier-Guided Attention Network for Crowd Count Estimation](https://mlsanity.com/paper/2407.06110/) (2024)
- [An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery](https://mlsanity.com/paper/2601.07975/) (2026)
- [FSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training](https://mlsanity.com/paper/2602.01540/) (2026)
- [Optimising CSRNet with parameter-free attention mechanisms for crowd counting in public transport](https://mlsanity.com/paper/2605.18349/) (2026)
- [VesselBench-800K: A Large-scale Perception Benchmark for Multimodal Vessel Detection, Counting, and Density Estimation](https://mlsanity.com/paper/2609.37003/) (2026)

## Abstract (arXiv)

Recently, crowd counting is a hot topic in crowd analysis. Many CNN-based counting algorithms attain good performance. However, these methods only focus on the local appearance features of crowd scenes but ignore the large-range pixel-wise contextual and crowd attention information. To remedy the above problems, in this paper, we introduce the Spatial-/Channel-wise Attention Models into the traditional Regression CNN to estimate the density map, which is named as "SCAR". It consists of two modules, namely Spatial-wise Attention Model (SAM) and Channel-wise Attention Model (CAM). The former can encode the pixel-wise context of the entire image to more accurately predict density maps at the pixel level. The latter attempts to extract more discriminative features among different channels, which aids model to pay attention to the head region, the core of crowd scenes. Intuitively, CAM alleviates the mistaken estimation for background regions. Finally, two types of attention information and traditional CNN's feature maps are integrated by a concatenation operation. Furthermore, the extensive experiments are conducted on four popular datasets, Shanghai Tech Part A/B, GCC, and UCF_CC_50 Dataset. The results show that the proposed method achieves state-of-the-art results.