# Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance

arXiv: https://arxiv.org/abs/2405.10589 · HTML: https://mlsanity.com/paper/2405.10589/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 56 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 30 of 38)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://mlsanity.com/paper/1802.10062/) (2018)
- [Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds](https://mlsanity.com/paper/1808.01050/) (2018)
- [Bayesian Loss for Crowd Count Estimation with Point Supervision](https://mlsanity.com/paper/1908.03684/) (2019)
- [Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework](https://mlsanity.com/paper/2107.12746/) (2021)
- [NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization](https://mlsanity.com/paper/2001.03360/) (2020)
- [Frequency Bias in Neural Networks for Input of Non-Uniform Density](https://mlsanity.com/paper/2003.04560/) (2020)
- [JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method](https://mlsanity.com/paper/2004.03597/) (2020)
- [An End-to-End Transformer Model for Crowd Localization](https://mlsanity.com/paper/2202.13065/) (2022)
- [Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection](https://mlsanity.com/paper/1906.07538/) (2019)
- [Boosting Crowd Counting via Multifaceted Attention](https://mlsanity.com/paper/2203.02636/) (2022)
- [Finding Tiny Faces](https://mlsanity.com/paper/1612.04402/) (2016)
- [Localization in the Crowd with Topological Constraints](https://mlsanity.com/paper/2012.12482/) (2020)
- [Point in, Box out: Beyond Counting Persons in Crowds](https://mlsanity.com/paper/1904.01333/) (2019)
- [UltraSR: Spatial Encoding is a Missing Key for Implicit Image Function-based Arbitrary-Scale Super-Resolution](https://mlsanity.com/paper/2103.12716/) (2021)
- [Where are the Blobs: Counting by Localization with Point Supervision](https://mlsanity.com/paper/1807.09856/) (2018)
- [Rethinking Spatial Invariance of Convolutional Networks for Object Counting](https://mlsanity.com/paper/2206.05253/) (2022)
- [From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer](https://mlsanity.com/paper/1908.06473/) (2019)
- [Point-Query Quadtree for Crowd Counting, Localization, and More](https://mlsanity.com/paper/2308.13814/) (2023)
- [NAS-Count: Counting-by-Density with Neural Architecture Search](https://mlsanity.com/paper/2003.00217/) (2020)
- [A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds](https://mlsanity.com/paper/2007.12831/) (2020)
- [Focal Inverse Distance Transform Maps for Crowd Localization](https://mlsanity.com/paper/2102.07925/) (2021)
- [AutoScale: Learning to Scale for Crowd Counting and Localization](https://mlsanity.com/paper/1912.09632/) (2019)
- [Weighing Counts: Sequential Crowd Counting by Reinforcement Learning](https://mlsanity.com/paper/2007.08260/) (2020)

## Built on by (most cited first, 22 of 22)

- [UNICBench: UNIfied Counting Benchmark for MLLM](https://mlsanity.com/paper/2603.00595/) (2026)
- [Boosting Adverse Weather Crowd Counting via Multi-queue Contrastive Learning](https://mlsanity.com/paper/2408.05956/) (2024)
- [Embodied Crowd Counting](https://mlsanity.com/paper/2503.08367/) (2025)
- [Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization](https://mlsanity.com/paper/2503.12441/) (2025)
- [L2HCount:Generalizing Crowd Counting from Low to High Crowd Density via Density Simulation](https://mlsanity.com/paper/2503.12935/) (2025)
- [Exploring Scale Shift in Crowd Localization under the Context of Domain Generalization](https://mlsanity.com/paper/2510.19330/) (2025)
- [2D Gaussians Spatial Transport for Point-supervised Density Regression](https://mlsanity.com/paper/2511.14477/) (2025)
- [CoDi -- an exemplar-conditioned diffusion model for low-shot counting](https://mlsanity.com/paper/2512.20153/) (2025)
- [TCFormer: A 5M-Parameter Transformer with Density-Guided Aggregation for Weakly-Supervised Crowd Counting](https://mlsanity.com/paper/2512.22203/) (2025)
- [Generative Adversarial Perturbations with Cross-paradigm Transferability on Localized Crowd Counting](https://mlsanity.com/paper/2603.24821/) (2026)
- [HAJJv2-CrowdCount: Zero-Shot Benchmark for Dense Crowd Counting](https://mlsanity.com/paper/2607.07322/) (2026)
- [Crowd Detection Using Very-Fine-Resolution Satellite Imagery](https://mlsanity.com/paper/2504.19546/) (2025)
- [Transformer-Based Dual-Optical Attention Fusion Crowd Head Point Counting and Localization Network](https://mlsanity.com/paper/2505.06937/) (2025)
- [Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation](https://mlsanity.com/paper/2508.03749/) (2025)
- [Local Information Matters: A Rethink of Crowd Counting](https://mlsanity.com/paper/2508.16970/) (2025)
- [RepSFNet : A Single Fusion Network with Structural Reparameterization for Crowd Counting](https://mlsanity.com/paper/2601.20369/) (2026)
- [FSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training](https://mlsanity.com/paper/2602.01540/) (2026)
- [SynMVCrowd: A Large Synthetic Benchmark for Multi-view Crowd Counting and Localization](https://mlsanity.com/paper/2603.23956/) (2026)
- [Dense Point-to-Mask Optimization with Reinforced Point Selection for Crowd Instance Segmentation](https://mlsanity.com/paper/2604.01742/) (2026)
- [Count Anything](https://mlsanity.com/paper/2605.30846/) (2026)
- [Pix2Pix-Hybrid: Structure-Guided Conditional Synthesis of Hajj Crowd Images with Multi-Channel Conditioning and Weak Attribute Supervision](https://mlsanity.com/paper/2606.14297/) (2026)
- [Multi-Modal Hyper-Graph Fusion for Low-Light Crowd Counting](https://mlsanity.com/paper/2606.18566/) (2026)

## Abstract (arXiv)

Crowd counting and localization have become increasingly important in computer vision due to their wide-ranging applications. While point-based strategies have been widely used in crowd counting methods, they face a significant challenge, i.e., the lack of an effective learning strategy to guide the matching process. This deficiency leads to instability in matching point proposals to target points, adversely affecting overall performance. To address this issue, we introduce an effective approach to stabilize the proposal-target matching in point-based methods. We propose Auxiliary Point Guidance (APG) to provide clear and effective guidance for proposal selection and optimization, addressing the core issue of matching uncertainty. Additionally, we develop Implicit Feature Interpolation (IFI) to enable adaptive feature extraction in diverse crowd scenarios, further enhancing the model's robustness and accuracy. Extensive experiments demonstrate the effectiveness of our approach, showing significant improvements in crowd counting and localization performance, particularly under challenging conditions. The source codes and trained models will be made publicly available.