| EBC-ZIP: Improving Blockwise Crowd Counting with Zero-Inflated Poisson Regression | ✓ Link | 47.81 | | 75.04 | EBC-ZIP-B | 2025-06-24 |
| Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance | ✓ Link | 48.8 | 76.7 | | APGCC | 2024-05-17 |
| Crowd Counting and Individual Localization Using Pseudo Square Label | | 49.9 | | 77.6 | PSL-Net | 2024-05-13 |
| FGENet: Fine-Grained Extraction Network for Congested Crowd Counting | | 51.66 | 85.00 | | FGENet | 2024-01-02 |
| VMambaCC: A Visual State Space Model for Crowd Counting | | 51.87 | 81.3 | | VMambaCC | 2024-05-07 |
| CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification | ✓ Link | 52.5 | | 85.9 | CLIP-EBC (ViT-B/16) | 2024-03-14 |
| Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework | ✓ Link | 52.74 | 85.06 | | P2PNet | 2021-07-27 |
| CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification | ✓ Link | 54.0 | | 83.2 | CLIP-EBC (ResNet50) | 2024-03-14 |
| Rethinking Spatial Invariance of Convolutional Networks for Object Counting | ✓ Link | 54.8 | 89.1 | | GauNet (ResNet-50) | 2022-06-10 |
| Improving Local Features with Relevant Spatial Information by Vision Transformer for Crowd Counting | ✓ Link | 54.8 | | 80.9 | LoViTCrowd | 2022-09-30 |
| Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting | ✓ Link | 57.55 | 94.48 | | M-SFANet+M-SegNet | 2020-03-12 |
| Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss | ✓ Link | 57.6 | | | SGANet + CL | 2019-11-18 |
| Crowd Counting via Segmentation Guided Attention Networks and Curriculum Loss | ✓ Link | 58 | | | SGANet | 2019-11-18 |
| From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer | ✓ Link | 58.3 | | | S-DCNet | 2019-08-15 |
| Learning Spatial Awareness to Improve Crowd Counting | | 59.4 | 92.5 | | SPANet | 2019-09-16 |
| Distribution Matching for Crowd Counting | ✓ Link | 59.7 | | | DM-Count | 2020-09-28 |
| FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion | ✓ Link | 62.2 | 101.2 | | FusionCount | 2022-02-28 |
| CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification | ✓ Link | 62.3 | | 98.9 | DMCount-EBC | 2024-03-14 |
| Context-Aware Crowd Counting | ✓ Link | 62.3 | | | CAN | 2018-11-26 |
| Improving Deep Regression with Ordinal Entropy | ✓ Link | 65.6 | 105.0 | | OrdinalEntropy | 2023-01-21 |
| CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification | ✓ Link | 66.3 | | 105.0 | CSRNet-EBC | 2024-03-14 |
| Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection | ✓ Link | 66.4 | | | LSC-CNN | 2019-06-18 |
| Scale Aggregation Network for Accurate and Efficient Crowd Counting | ✓ Link | 67.0 | | | SANet | 2018-09-01 |
| CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes | ✓ Link | 68.2 | | | CSRNet | 2018-02-27 |
| Iterative Crowd Counting | | 68.5 | | | ic-CNN | 2018-07-26 |
| Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN | | 72.5 | | | IG-CNN | 2018-07-26 |
| Crowd Counting With Deep Negative Correlation Learning | ✓ Link | 73.5 | | | D-ConvNet | 2018-06-01 |
| Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs | | 73.6 | | | CP-CNN | 2017-08-02 |
| Leveraging Unlabeled Data for Crowd Counting by Learning to Rank | ✓ Link | 73.6 | | | Liu et al. | 2018-03-08 |
| Few-shot Object Counting with Similarity-Aware Feature Enhancement | ✓ Link | 73.70 | | | SAFECount | 2022-01-22 |
| Crowd Counting via Adversarial Cross-Scale Consistency Pursuit | ✓ Link | 75.7 | | | ACSCP | 2018-06-01 |
| Switching Convolutional Neural Network for Crowd Counting | ✓ Link | 90.4 | | | Switch-CNN | 2017-08-01 |
| CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting | ✓ Link | 101.3 | 152.4 | | Cascaded-MTL | 2017-07-30 |
| Single-Image Crowd Counting via Multi-Column Convolutional Neural Network | ✓ Link | 110.2 | | | MCNN | 2016-01-01 |
| Cross-Scene Crowd Counting via Deep Convolutional Neural Networks | | 181.8 | | | Zhang et al. | 2015-06-01 |