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Crowd Counting

Crowd Counting is a task to count people in image. It is mainly used in real-life for automated public monitoring such as surveillance and traffic control. Different from object detection, Crowd Counting aims at recognizing arbitrarily sized targets in various situations including sparse and cluttering scenes at the same time.

Source: Deep Density-aware Count Regressor

Papers

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Showing 101–150 of 371 papers

TitleStatusHype
Semi-supervised Crowd Counting via Density AgencyCode1
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting—0
Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training—0
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting—0
Multi-scale Feature Aggregation for Crowd Counting—0
Redesigning Multi-Scale Neural Network for Crowd CountingCode1
Discrete-Constrained Regression for Local Counting ModelsCode0
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City ApplicationsCode0
The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network—0
Backdoor Attacks on Crowd CountingCode1
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation—0
An Improved Normed-Deformable Convolution for Crowd CountingCode0
Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes—0
Reducing Capacity Gap in Knowledge Distillation with Review Mechanism for Crowd CountingCode0
Rethinking Spatial Invariance of Convolutional Networks for Object CountingCode1
Self-supervised Domain Adaptation in Crowd Counting—0
Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting—0
Fine-Grained Counting with Crowd-Sourced Supervision—0
Bi-level Alignment for Cross-Domain Crowd CountingCode1
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting—0
Cross-View Cross-Scene Multi-View Crowd Counting—0
Crowd counting with segmentation attention convolutional neural network—0
Crowd counting with crowd attention convolutional neural network—0
SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing—0
Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches—0
DR.VIC: Decomposition and Reasoning for Video Individual CountingCode1
CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP—0
Joint CNN and Transformer Network via weakly supervised Learning for efficient crowd counting—0
CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability—0
Boosting Crowd Counting via Multifaceted AttentionCode1
FusionCount: Efficient Crowd Counting via Multiscale Feature FusionCode1
Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting—0
Multiscale Crowd Counting and Localization By Multitask Point SupervisionCode1
TAFNet: A Three-Stream Adaptive Fusion Network for RGB-T Crowd CountingCode0
A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting—0
Enhancing and Dissecting Crowd Counting By Synthetic Data—0
Few-shot Object Counting with Similarity-Aware Feature EnhancementCode1
BBA-net: A bi-branch attention network for crowd counting—0
Deep Rank-Consistent Pyramid Model for Enhanced Crowd CountingCode1
Crowd Counting in the Frequency DomainCode1
Scene-Adaptive Attention Network for Crowd Counting—0
Towards More Effective PRM-based Crowd Counting via A Multi-resolution Fusion and Attention Network—0
Panoptic Segmentation: A ReviewCode1
PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting—0
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines—0
Vicinal Counting Networks—0
CCTrans: Simplifying and Improving Crowd Counting with TransformerCode1
Harnessing Perceptual Adversarial Patches for Crowd CountingCode1
Audio-Visual Transformer Based Crowd Counting—0
Semi-Supervised Crowd Counting from Unlabeled Data—0
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