Crowd Counting论文小结（持续更新）

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CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested

CCTrans: Simplifying and Improving Crowd Counting with Transformer

Boosting Crowd Counting with Transformers

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Focal Inverse Distance Transform Maps for Crowd Localization and Counting in Dense Crowd

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Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting

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PSCNet: Pyramidal Scale and Global Context Guided Network for Crowd Counting

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DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting

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Scale Aggregation Network for Accurate and Efficient Crowd Counting

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Audio-visual Representation Learning for Anomaly Events Detection in Crowds

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Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

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Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework

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Adaptive Dilated Network with Self-Correction Supervision for Counting

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Scene-Adaptive Attention Network for Crowd Counting

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Multi-Level Attentive Convoluntional Neural Network for Crowd Counting

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Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting

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Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting

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Attention Scaling for Crowd Counting

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TransCrowd: Weakly-Supervised Crowd Counting with Transformer

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Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer

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Spatial Uncertainty-Aware Semi-Supervised Crowd Counting