Crowd Counting论文小结（持续更新）
- CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested
- CCTrans: Simplifying and Improving Crowd Counting with Transformer
- Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd
- Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
- PSCNet: Pyramidal Scale and Global Context Guided Network for Crowd Counting
- DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting
- Scale Aggregation Network for Accurate and Efficient Crowd Counting
- Audio-visual Representation Learning for Anomaly Events Detection in Crowds
- Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
- Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework
- Adaptive Dilated Network with Self-Correction Supervision for Counting
- Scene-Adaptive Attention Network for Crowd Counting
- Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
- Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
- Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting
- Attention Scaling for Crowd Counting
- TransCrowd: Weakly-Supervised Crowd Counting with Transformer
- Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer
- Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested
CCTrans: Simplifying and Improving Crowd Counting with Transformer
Boosting Crowd Counting with Transformers
Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
PSCNet: Pyramidal Scale and Global Context Guided Network for Crowd Counting
DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting
Scale Aggregation Network for Accurate and Efficient Crowd Counting
Audio-visual Representation Learning for Anomaly Events Detection in Crowds
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework
Adaptive Dilated Network with Self-Correction Supervision for Counting
Scene-Adaptive Attention Network for Crowd Counting
Multi-Level Attentive Convoluntional Neural Network for Crowd Counting
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting
Attention Scaling for Crowd Counting
TransCrowd: Weakly-Supervised Crowd Counting with Transformer
Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
