{"total":43,"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":"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":"2104.09116","title":"TransCrowd: weakly-supervised crowd counting with transformers","created":"2021-04-19","cited_by_count":61,"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":"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":"2308.13814","title":"Point-Query Quadtree for Crowd Counting, Localization, and More","created":"2023-08-26","cited_by_count":40,"area":null},{"id":"2109.14483","title":"CCTrans: Simplifying and Improving Crowd Counting with Transformer","created":"2021-09-29","cited_by_count":30,"area":null},{"id":"2204.13349","title":"Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor","created":"2022-04-28","cited_by_count":27,"area":null},{"id":"2103.16291","title":"Leveraging Self-Supervision for Cross-Domain Crowd Counting","created":"2021-03-30","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":"2107.12619","title":"Uniformity in Heterogeneity:Diving Deep into Count Interval Partition for Crowd Counting","created":"2021-07-27","cited_by_count":20,"area":null},{"id":"2207.09865","title":"Discrete-Constrained Regression for Local Counting Models","created":"2022-07-20","cited_by_count":19,"area":null},{"id":"2208.02894","title":"Redesigning Multi-Scale Neural Network for Crowd Counting","created":"2022-08-04","cited_by_count":17,"area":null},{"id":"2107.03665","title":"Crowd Counting via Perspective-Guided Fractional-Dilation Convolution","created":"2021-07-08","cited_by_count":15,"area":null},{"id":"2108.08023","title":"Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting","created":"2021-08-18","cited_by_count":15,"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":"2112.15509","title":"Scene-Adaptive Attention Network for Crowd Counting","created":"2021-12-31","cited_by_count":12,"area":null},{"id":"2403.09281","title":"CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification","created":"2024-03-14","cited_by_count":12,"area":null},{"id":"2207.05641","title":"Backdoor Attacks on Crowd Counting","created":"2022-07-12","cited_by_count":10,"area":null},{"id":"2304.11677","title":"RGB-D Indiscernible Object Counting in Underwater Scenes","created":"2023-04-23","cited_by_count":9,"area":null},{"id":"2310.17154","title":"Deep Imbalanced Regression via Hierarchical Classification Adjustment","created":"2023-10-26","cited_by_count":9,"area":null},{"id":"2403.18074","title":"Every Shot Counts: Using Exemplars for Repetition Counting in Videos","created":"2024-03-26","cited_by_count":9,"area":null},{"id":"2105.10926","title":"Rethinking Global Context in Crowd Counting","created":"2021-05-23","cited_by_count":8,"area":null},{"id":"2203.08219","title":"CrowdMLP: Weakly-Supervised Crowd Counting via Multi-Granularity MLP","created":"2022-03-15","cited_by_count":8,"area":null},{"id":"2402.15297","title":"Semi-supervised Counting via Pixel-by-pixel Density Distribution Modelling","created":"2024-02-23","cited_by_count":7,"area":null},{"id":"2207.00176","title":"End-to-end cell recognition by point annotation","created":"2022-07-01","cited_by_count":6,"area":null},{"id":"2310.10352","title":"Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes","created":"2023-10-16","cited_by_count":6,"area":null},{"id":"2201.04819","title":"Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting","created":"2022-01-13","cited_by_count":5,"area":null},{"id":"2108.08784","title":"Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting","created":"2021-08-19","cited_by_count":4,"area":null},{"id":"2212.04127","title":"Progressive Multi-resolution Loss for Crowd Counting","created":"2022-12-08","cited_by_count":4,"area":null},{"id":"2110.14613","title":"International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines","created":"2021-10-27","cited_by_count":3,"area":null},{"id":"2212.11542","title":"Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks","created":"2022-12-22","cited_by_count":3,"area":null},{"id":"2405.02296","title":"M\\\"obius Transform for Mitigating Perspective Distortions in Representation Learning","created":"2024-03-07","cited_by_count":2,"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":"2111.00406","title":"PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting","created":"2021-10-31","cited_by_count":0,"area":null},{"id":"2204.04653","title":"Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches","created":"2022-04-10","cited_by_count":0,"area":null},{"id":"2210.09796","title":"Inception-Based Crowd Counting -- Being Fast while Remaining Accurate","created":"2022-10-18","cited_by_count":0,"area":null},{"id":"2211.06835","title":"Scale-Aware Crowd Counting Using a Joint Likelihood Density Map and Synthetic Fusion Pyramid Network","created":"2022-11-13","cited_by_count":0,"area":null},{"id":"2312.16771","title":"Scale-Aware Crowd Count Network with Annotation Error Correction","created":"2023-12-28","cited_by_count":0,"area":null},{"id":"2602.01540","title":"FSCA-Net: Feature-Separated Cross-Attention Network for Robust Multi-Dataset Training","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.23956","title":"SynMVCrowd: A Large Synthetic Benchmark for Multi-view Crowd Counting and Localization","created":"2026-03-25","cited_by_count":0,"area":null}],"next_cursor":null}