{"total":68,"rows":[{"id":"2107.12746","title":"Rethinking Counting and Localization in Crowds:A Purely Point-Based Framework","created":"2021-07-27","cited_by_count":152,"area":null},{"id":"2104.08391","title":"Learning To Count Everything","created":"2021-04-16","cited_by_count":150,"area":null},{"id":"2203.08354","title":"Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting","created":"2022-03-16","cited_by_count":80,"area":null},{"id":"2202.13065","title":"An End-to-End Transformer Model for Crowd Localization","created":"2022-02-26","cited_by_count":74,"area":null},{"id":"2211.08217","title":"A Low-Shot Object Counting Network With Iterative Prototype Adaptation","created":"2022-11-15","cited_by_count":65,"area":null},{"id":"2104.09116","title":"TransCrowd: weakly-supervised crowd counting with transformers","created":"2021-04-19","cited_by_count":61,"area":null},{"id":"2303.02001","title":"Zero-shot Object Counting","created":"2023-03-03","cited_by_count":57,"area":null},{"id":"2207.10988","title":"Few-shot Object Counting and Detection","created":"2022-07-22","cited_by_count":55,"area":null},{"id":"2206.05253","title":"Rethinking Spatial Invariance of Convolutional Networks for Object Counting","created":"2022-06-10","cited_by_count":54,"area":null},{"id":"2211.15272","title":"Topologically faithful image segmentation via induced matching of persistence barcodes","created":"2022-11-28","cited_by_count":49,"area":null},{"id":"2304.04231","title":"CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model","created":"2023-04-09","cited_by_count":47,"area":"mllm"},{"id":"2308.10468","title":"STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning","created":"2023-08-21","cited_by_count":41,"area":null},{"id":"2110.08335","title":"Trigger Hunting with a Topological Prior for Trojan Detection","created":"2021-10-15","cited_by_count":39,"area":"safety"},{"id":"2102.07925","title":"Focal Inverse Distance Transform Maps for Crowd Localization","created":"2021-02-16","cited_by_count":36,"area":null},{"id":"2205.14212","title":"Exemplar Free Class Agnostic Counting","created":"2022-05-27","cited_by_count":32,"area":null},{"id":"2305.04440","title":"Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting","created":"2023-05-08","cited_by_count":25,"area":null},{"id":"2110.04886","title":"Multi-Class Cell Detection Using Spatial Context Representation","created":"2021-10-10","cited_by_count":24,"area":null},{"id":"2308.09887","title":"Calibrating Uncertainty for Semi-Supervised Crowd Counting","created":"2023-08-19","cited_by_count":22,"area":null},{"id":"2405.10589","title":"Improving Point-based Crowd Counting and Localization Based on Auxiliary Point Guidance","created":"2024-05-17","cited_by_count":22,"area":null},{"id":"2303.12790","title":"$CrowdDiff$: Multi-hypothesis Crowd Density Estimation using Diffusion Models","created":"2023-03-22","cited_by_count":20,"area":"diffusion"},{"id":"2311.13596","title":"T-Rex: Counting by Visual Prompting","created":"2023-11-22","cited_by_count":20,"area":null},{"id":"2203.12335","title":"DR.VIC: Decomposition and Reasoning for Video Individual Counting","created":"2022-03-23","cited_by_count":18,"area":null},{"id":"2306.01209","title":"Counting Crowds in Bad Weather","created":"2023-06-02","cited_by_count":14,"area":null},{"id":"2308.16187","title":"Boosting Detection in Crowd Analysis via Underutilized Output Features","created":"2023-08-30","cited_by_count":14,"area":null},{"id":"2107.08645","title":"Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark","created":"2021-07-19","cited_by_count":13,"area":null},{"id":"2110.14862","title":"Audio-visual Representation Learning for Anomaly Events Detection in Crowds","created":"2021-10-28","cited_by_count":13,"area":null},{"id":"2206.05717","title":"Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher","created":"2022-06-12","cited_by_count":12,"area":null},{"id":"2108.00584","title":"Congested Crowd Instance Localization with Dilated Convolutional Swin Transformer","created":"2021-08-02","cited_by_count":11,"area":null},{"id":"2107.12858","title":"Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network","created":"2021-07-27","cited_by_count":10,"area":null},{"id":"2110.06295","title":"Persistent Homology with Improved Locality Information for more Effective Delineation","created":"2021-10-12","cited_by_count":10,"area":null},{"id":"2410.16646","title":"TopoDiffusionNet: A Topology-aware Diffusion Model","created":"2024-10-22","cited_by_count":10,"area":"diffusion"},{"id":"2503.12460","title":"Exploring Contextual Attribute Density in Referring Expression Counting","created":"2025-03-16","cited_by_count":10,"area":null},{"id":"2309.13097","title":"Zero-Shot Object Counting with Language-Vision Models","created":"2023-09-22","cited_by_count":9,"area":null},{"id":"2203.03768","title":"CrowdFormer: Weakly-supervised Crowd counting with Improved Generalizability","created":"2022-03-07","cited_by_count":8,"area":null},{"id":"2212.04067","title":"Consistency-Aware Anchor Pyramid Network for Crowd Localization","created":"2022-12-08","cited_by_count":8,"area":null},{"id":"2303.02602","title":"DPA-P2PNet: Deformable Proposal-aware P2PNet for Accurate Point-based Cell Detection","created":"2023-03-05","cited_by_count":8,"area":null},{"id":"2309.05277","title":"Interactive Class-Agnostic Object Counting","created":"2023-09-11","cited_by_count":8,"area":null},{"id":"2311.04509","title":"Learning Discriminative Features for Crowd Counting","created":"2023-11-08","cited_by_count":8,"area":null},{"id":"2405.03978","title":"VMambaCC: A Visual State Space Model for Crowd Counting","created":"2024-05-07","cited_by_count":7,"area":"ssm"},{"id":"2509.13939","title":"Can Current AI Models Count What We Mean, Not What They See? A Benchmark and Systematic Evaluation","created":"2025-09-17","cited_by_count":7,"area":null},{"id":"2505.21943","title":"Point-to-Region Loss for Semi-Supervised Point-Based Crowd Counting","created":"2025-05-28","cited_by_count":6,"area":null},{"id":"2205.14659","title":"Glance to Count: Learning to Rank with Anchors for Weakly-supervised Crowd Counting","created":"2022-05-29","cited_by_count":5,"area":null},{"id":"2311.10011","title":"SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting","created":"2023-11-16","cited_by_count":5,"area":null},{"id":"2405.11770","title":"Learning Spatial Similarity Distribution for Few-shot Object Counting","created":"2024-05-20","cited_by_count":5,"area":null},{"id":"2505.15325","title":"SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition","created":"2025-05-21","cited_by_count":4,"area":null},{"id":"2110.04727","title":"LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds","created":"2021-10-10","cited_by_count":3,"area":null},{"id":"2603.00595","title":"UNICBench: UNIfied Counting Benchmark for MLLM","created":"2026-02-28","cited_by_count":3,"area":"mllm"},{"id":"2212.02248","title":"Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects","created":"2022-12-02","cited_by_count":2,"area":null},{"id":"2404.13992","title":"Dynamic Proxy Domain Generalizes the Crowd Localization by Better Binary Segmentation","created":"2024-04-22","cited_by_count":2,"area":null},{"id":"2405.02301","title":"TFCounter:Polishing Gems for Training-Free Object Counting","created":"2024-03-12","cited_by_count":2,"area":null},{"id":"2505.22850","title":"What is the Right Embedding Space for Contrastive Learning in Referring Expression Counting?","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2203.04061","title":"Counting with Adaptive Auxiliary Learning","created":"2022-03-08","cited_by_count":1,"area":null},{"id":"2204.07406","title":"SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard Example Focusing","created":"2022-04-15","cited_by_count":1,"area":null},{"id":"2208.05256","title":"Multi-scale Feature Aggregation for Crowd Counting","created":"2022-08-10","cited_by_count":1,"area":null},{"id":"2503.12168","title":"Learning Extremely High Density Crowds as Active Matters","created":"2025-03-15","cited_by_count":1,"area":null},{"id":"2503.12441","title":"Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization","created":"2025-03-16","cited_by_count":1,"area":null},{"id":"2511.12702","title":"Counting Through Occlusion: Framework for Open World Amodal Counting","created":"2025-11-16","cited_by_count":1,"area":null},{"id":"2206.05648","title":"Indirect-Instant Attention Optimization for Crowd Counting in Dense Scenes","created":"2022-06-12","cited_by_count":0,"area":null},{"id":"2307.07677","title":"Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting","created":"2023-07-15","cited_by_count":0,"area":null},{"id":"2308.00530","title":"Tolerating Annotation Displacement in Dense Object Counting via Point Annotation Probability Map","created":"2023-07-29","cited_by_count":0,"area":null},{"id":"2407.08364","title":"Scalar Function Topology Divergence: Comparing Topology of 3D Objects","created":"2024-07-11","cited_by_count":0,"area":null},{"id":"2503.12061","title":"EHNet: An Efficient Hybrid Network for Crowd Counting and Localization","created":"2025-03-15","cited_by_count":0,"area":null},{"id":"2503.14523","title":"SDF-TopoNet: A Two-Stage Framework for Tubular Structure Segmentation via SDF Pre-training and Topology-Aware Fine-Tuning","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2504.19546","title":"Crowd Detection Using Very-Fine-Resolution Satellite Imagery","created":"2025-04-28","cited_by_count":0,"area":null},{"id":"2509.02261","title":"DSGC-Net: A Dual-Stream Graph Convolutional Network for Crowd Counting via Feature Correlation Mining","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2601.07975","title":"An Efficient Additive Kolmogorov-Arnold Transformer for Point-Level Maize Localization in Unmanned Aerial Vehicle Imagery","created":"2026-01-12","cited_by_count":0,"area":null},{"id":"2603.16241","title":"Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting","created":"2026-03-17","cited_by_count":0,"area":null},{"id":"2606.15592","title":"DenseControl: Instance-Level Controllable Synthesis of Dense Crowd Image","created":"2026-06-14","cited_by_count":0,"area":null}],"next_cursor":null}