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Top Conference/Journal

This repository is for listing papers of crowd counting published on CVPR, ICCV, ECCV, T-PAMI and IJCV.

CVPR

  • [Crowd-Hat] Boosting Detection in Crowd Analysis via Underutilized Output Features (CVPR)[paper][code]
  • [ChfL] Crowd Counting in the Frequency Domain (CVPR) [paper][code]
  • [GauNet] Rethinking Spatial Invariance of Convolutional Networks for Object Counting (CVPR) [paper][code]
  • [DR.VIC] DR.VIC: Decomposition and Reasoning for Video Individual Counting (CVPR) [paper][code]
  • [CDCC] Leveraging Self-Supervision for Cross-Domain Crowd Counting (CVPR) [paper][code]
  • [MAN] Boosting Crowd Counting via Multifaceted Attention (CVPR) [paper][code]
  • [BLA] Bi-level Alignment for Cross-Domain Crowd Counting (CVPR) [paper][code]
  • [BMNet] Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting (CVPR)[paper][code]
  • [GLoss] A Generalized Loss Function for Crowd Counting and Localization (CVPR) [paper]
  • [CVCS] Cross-View Cross-Scene Multi-View Crowd Counting (CVPR) [paper]
  • [STANet] Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark (CVPR) [paper][code]
  • [RGBT-CC] Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting (CVPR) [paper][code]
  • [STANet] Detection, Tracking, and Counting Meets Drones in Crowds: A Benchmark (CVPR) [paper][code]
  • [GLoss] A Generalized Loss Function for Crowd Counting and Localization (CVPR) [paper]
  • [CVCS] Cross-View Cross-Scene Multi-View Crowd Counting (CVPR) [paper]
  • [ADSCNet] Adaptive Dilated Network with Self-Correction Supervision for Counting (CVPR) [paper]
  • [RPNet] Reverse Perspective Network for Perspective-Aware Object Counting (CVPR) [paper] [code]
  • [ASNet] Attention Scaling for Crowd Counting (CVPR) [paper] [code]
  • [RAZ-Net] Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization (CVPR) [paper]
  • [RDNet] Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization (CVPR) [paper][code]
  • [RRSP] Residual Regression with Semantic Prior for Crowd Counting (CVPR) [paper][code]
  • [MVMS] Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs (CVPR) [paper] [Project] [Dataset&Code]
  • [AT-CFCN] Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting (CVPR) [paper]
  • [TEDnet] Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks (CVPR) [paper]
  • [CAN] Context-Aware Crowd Counting (CVPR) [paper] [code]
  • [PACNN] Revisiting Perspective Information for Efficient Crowd Counting (CVPR)[paper]
  • [PSDDN] Point in, Box out: Beyond Counting Persons in Crowds (CVPR(oral))[paper]
  • [ADCrowdNet] ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding (CVPR) [paper]
  • [CCWld, SFCN] Learning from Synthetic Data for Crowd Counting in the Wild (CVPR) [paper] [Project] [arxiv]
  • [CSR] CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes (CVPR) [paper] [code]
  • [L2R] Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (CVPR) [paper] [code]
  • [ACSCP] Crowd Counting via Adversarial Cross-Scale Consistency Pursuit (CVPR) [paper] [unofficial code: PyTorch]
  • [DecideNet] DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density (CVPR) [paper]
  • [D-ConvNet] Crowd Counting with Deep Negative Correlation Learning (CVPR) [paper] [code]
  • [IG-CNN] Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN (CVPR) [paper]
  • [Switching CNN] Switching Convolutional Neural Network for Crowd Counting (CVPR) [paper] [code]
  • [MCNN] Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (CVPR) [paper] [unofficial code: TensorFlow PyTorch]
  • [Zhang 2015] Cross-scene Crowd Counting via Deep Convolutional Neural Networks (CVPR) [paper] [code]
  • [Idrees 2013] Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images (CVPR) [paper]
  • [Ma 2013] Crossing the Line: Crowd Counting by Integer Programming with Local Features (CVPR) [paper]
  • [Chen 2013] Cumulative Attribute Space for Age and Crowd Density Estimation (CVPR) [paper]
  • [Chan 2008] Privacy preserving crowd monitoring: Counting people without people models or tracking (CVPR) [paper]

ICCV

  • [URC] Crowd Counting With Partial Annotations in an Image (ICCV) [paper]
  • [MFDC] Exploiting Sample Correlation for Crowd Counting With Multi-Expert Network (ICCV) [paper]
  • [SDNet] Towards A Universal Model for Cross-Dataset Crowd Counting (ICCV) [paper]
  • [P2PNet] Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework (ICCV(Oral)) [paper][code]
  • [UEPNet] Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting (ICCV) [paper][code]
  • [SUA] Spatial Uncertainty-Aware Semi-Supervised Crowd Counting (ICCV) [paper][code]
  • [DKPNet] Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV) [paper][code]
  • [P2PNet] Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework (ICCV(Oral)) [paper][code]
  • [UEPNet] Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting (ICCV) [paper][code]
  • [SUA] Spatial Uncertainty-Aware Semi-Supervised Crowd Counting (ICCV) [paper][code]
  • [CG-DRCN] Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method (ICCV)[paper]
  • [ADMG] Adaptive Density Map Generation for Crowd Counting (ICCV)[paper]
  • [DSSINet] Crowd Counting with Deep Structured Scale Integration Network (ICCV) [paper][code]
  • [RANet] Relational Attention Network for Crowd Counting (ICCV)[paper]
  • [ANF] Attentional Neural Fields for Crowd Counting (ICCV)[paper]
  • [SPANet] Learning Spatial Awareness to Improve Crowd Counting (ICCV(oral)) [paper]
  • [MBTTBF] Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting (ICCV) [paper]
  • [CFF] Counting with Focus for Free (ICCV) [paper][code]
  • [L2SM] Learn to Scale: Generating Multipolar Normalized Density Map for Crowd Counting (ICCV) [paper]
  • [S-DCNet] From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer (ICCV) [paper][code]
  • [BL] Bayesian Loss for Crowd Count Estimation with Point Supervision (ICCV(oral)) [paper][code]
  • [PGCNet] Perspective-Guided Convolution Networks for Crowd Counting (ICCV) [paper][code]
  • [CP-CNN] Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs (ICCV) [paper]
  • [ConvLSTM] Spatiotemporal Modeling for Crowd Counting in Videos (ICCV) [paper]
  • [COUNT Forest] COUNT Forest: CO-voting Uncertain Number of Targets using Random Forest for Crowd Density Estimation (ICCV) [paper]
  • [Bayesian] Bayesian Model Adaptation for Crowd Counts (ICCV) [paper]
  • [SSR] From Semi-Supervised to Transfer Counting of Crowds (ICCV) [paper]
  • [Rodriguez 2011] Density-aware person detection and tracking in crowds (ICCV) [paper]

ECCV

  • [CLTR] An End-to-End Transformer Model for Crowd Localization (ECCV) [paper] [code]
  • [CF-MVCC] Calibration-free Multi-view Crowd Counting (ECCV) [paper]
  • [DC] Discrete-Constrained Regression for Local Counting Models (ECCV) [paper]
  • [AMSNet] NAS-Count: Counting-by-Density with Neural Architecture Search (ECCV) [paper]
  • [AMRNet] Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting (ECCV) [paper][code]
  • [LibraNet] Weighting Counts: Sequential Crowd Counting by Reinforcement Learning (ECCV) [paper][code]
  • [GP] Learning to Count in the Crowd from Limited Labeled Data (ECCV) [paper]
  • [IRAST] Semi-supervised Crowd Counting via Self-training on Surrogate Tasks (ECCV) [paper]
  • [PSSW] Active Crowd Counting with Limited Supervision (ECCV) [paper]
  • [CCLS] Weakly-Supervised Crowd Counting Learns from Sorting rather than Locations (ECCV) [paper]
  • [SANet] Scale Aggregation Network for Accurate and Efficient Crowd Counting (ECCV) [paper]
  • [ic-CNN] Iterative Crowd Counting (ECCV) [paper]
  • [CL] Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (ECCV) [paper]
  • [LCFCN] Where are the Blobs: Counting by Localization with Point Supervision (ECCV) [paper] [code]
  • [Hydra-CNN] Towards perspective-free object counting with deep learning (ECCV) [paper] [code]
  • [CNN-Boosting] Learning to Count with CNN Boosting (ECCV) [paper]
  • [Crossing-line] Crossing-line Crowd Counting with Two-phase Deep Neural Networks (ECCV) [paper]
  • [GP] Gaussian Process Density Counting from Weak Supervision (ECCV) [paper]
  • [Arteta 2014] Interactive Object Counting (ECCV) [paper]

T-PAMI

  • [DPDNet] Locating and Counting Heads in Crowds With a Depth Prior (T-PAMI) [paper] [code]
  • [EPF] Counting People by Estimating People Flows (T-PAMI) [paper][code]
  • [LA-Batch] Locality-Aware Crowd Counting (T-PAMI) [paper]
  • [JHU-CROWD] JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (T-PAMI) [paper](extension of CG-DRCN)
  • [KDMG] Kernel-based Density Map Generation for Dense Object Counting (T-PAMI) [paper][code]
  • [NWPU] NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization (T-PAMI) [paper][code]
  • [LSC-CNN] Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection (T-PAMI) [paper][code]
  • [D-ConvNet] Nonlinear Regression via Deep Negative Correlation Learning (T-PAMI) [paper](extension of D-ConvNet)[Project]
  • [SL2R] Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank (T-PAMI) [paper](extension of L2R)

IJCV

  • [PWCU] Pixel-wise Crowd Understanding via Synthetic Data (IJCV) [paper]
  • [AutoScale] AutoScale: Learning to Scale for Crowd Counting (IJCV) [paper] (extension of L2SM)[code]