{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/crowd-counting/papers/4","list_of":"/task/crowd-counting","task":"Crowd Counting","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":4,"pages_in_order":4,"rows_per_page":100,"rows":[301,371],"of":371,"counts":{"archive_papers_tagged":371,"with_a_code_link":154,"where_syntology_ran_a_sample":22,"not_listed_spam_title":0,"listed":371,"listed_where_code_ran":22,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/crowd-counting","prev":"/task/crowd-counting/papers/3","next":null,"papers":[{"url":null,"slug":"count-guided-weakly-supervised-localization","title":"Count-guided Weakly Supervised Localization Based on Density Map","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-the-learning-of-multi-column","title":"Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting","date":"2019-09-17","arxiv_id":"1909.07608","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-understanding-crowd-mobility-in-smart","title":"Toward Understanding Crowd Mobility in Smart Cities through the Internet of Things","date":"2019-09-17","arxiv_id":"1909.08522","repositories_listed":0,"syntology":null},{"url":"/paper/learning-spatial-awareness-to-improve-crowd","slug":"learning-spatial-awareness-to-improve-crowd","title":"Learning Spatial Awareness to Improve Crowd Counting","date":"2019-09-16","arxiv_id":"1909.07057","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-bottom-top-and-top-bottom-feature","title":"Multi-Level Bottom-Top and Top-Bottom Feature Fusion for Crowd Counting","date":"2019-08-28","arxiv_id":"1908.10937","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-regression-via-deep-negative","title":"Robust Regression via Deep Negative Correlation Learning","date":"2019-08-24","arxiv_id":"1908.09066","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-with-deep-structured-scale","title":"Crowd Counting with Deep Structured Scale Integration Network","date":"2019-08-23","arxiv_id":"1908.08692","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-3d-convolutional-networks-for-crowd","title":"Enhanced 3D convolutional networks for crowd counting","date":"2019-08-12","arxiv_id":"1908.04121","repositories_listed":0,"syntology":null},{"url":null,"slug":"scar-spatial-channel-wise-attention","title":"SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting","date":"2019-08-10","arxiv_id":"1908.03716","repositories_listed":0,"syntology":null},{"url":null,"slug":"attend-to-count-crowd-counting-with-adaptive","title":"Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs","date":"2019-08-07","arxiv_id":"1908.02797","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-to-scale-generating-multipolar","title":"Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting","date":"2019-07-29","arxiv_id":"1907.12428","repositories_listed":0,"syntology":null},{"url":null,"slug":"ha-ccn-hierarchical-attention-based-crowd","title":"HA-CCN: Hierarchical Attention-based Crowd Counting Network","date":"2019-07-24","arxiv_id":"1907.10255","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-crowd-counting-via-dynamic-temporal","title":"Fast Video Crowd Counting with a Temporal Aware Network","date":"2019-07-04","arxiv_id":"1907.02198","repositories_listed":0,"syntology":null},{"url":null,"slug":"inverse-attention-guided-deep-crowd-counting","title":"Inverse Attention Guided Deep Crowd Counting Network","date":"2019-07-02","arxiv_id":"1907.01193","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-aware-density-map-for-crowd-counting","title":"Content-aware Density Map for Crowd Counting and Density Estimation","date":"2019-06-17","arxiv_id":"1906.07258","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-and-density-estimation-by-1","title":"Crowd Counting and Density Estimation by Trellis Encoder-Decoder Networks","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"density-map-regression-guided-detection","title":"Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-heterogeneous-auxiliary-tasks-to","title":"Leveraging Heterogeneous Auxiliary Tasks to Assist Crowd Counting","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-attentive-zooming-for-joint-crowd","title":"Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/wide-area-crowd-counting-via-ground-plane","slug":"wide-area-crowd-counting-via-ground-plane","title":"Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"counting-and-segmenting-sorghum-heads","title":"Counting and Segmenting Sorghum Heads","date":"2019-05-30","arxiv_id":"1905.13291","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-density-estimation-using-novel-feature","title":"Crowd Density Estimation using Novel Feature Descriptor","date":"2019-05-15","arxiv_id":"1905.05891","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-transformer-network","title":"Crowd Transformer Network","date":"2019-04-04","arxiv_id":"1904.02774","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-in-box-out-beyond-counting-persons-in","title":"Point in, Box out: Beyond Counting Persons in Crowds","date":"2019-04-02","arxiv_id":"1904.01333","repositories_listed":0,"syntology":null},{"url":null,"slug":"w-net-reinforced-u-net-for-density-map","title":"W-Net: Reinforced U-Net for Density Map Estimation","date":"2019-03-27","arxiv_id":"1903.11249","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-with-decomposed-uncertainty","title":"Crowd Counting with Decomposed Uncertainty","date":"2019-03-15","arxiv_id":"1903.07427","repositories_listed":0,"syntology":null},{"url":null,"slug":"deepcount-crowd-counting-with-wifi-via-deep","title":"DeepCount: Crowd Counting with WiFi via Deep Learning","date":"2019-03-13","arxiv_id":"1903.05316","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-synthetic-data-for-crowd","title":"Learning from Synthetic Data for Crowd Counting in the Wild","date":"2019-03-08","arxiv_id":"1903.03303","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-using-scale-aware-attention","title":"Crowd Counting Using Scale-Aware Attention Networks","date":"2019-03-05","arxiv_id":"1903.02025","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-and-density-estimation-by","title":"Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network","date":"2019-03-03","arxiv_id":"1903.00853","repositories_listed":0,"syntology":null},{"url":null,"slug":"scale-aware-attention-network-for-crowd","title":"Multi-Scale Attention Network for Crowd Counting","date":"2019-01-17","arxiv_id":"1901.06026","repositories_listed":0,"syntology":null},{"url":null,"slug":"dafe-fd-density-aware-feature-enrichment-for","title":"DAFE-FD: Density Aware Feature Enrichment for Face Detection","date":"2019-01-16","arxiv_id":"1901.05375","repositories_listed":0,"syntology":null},{"url":null,"slug":"mask-aware-networks-for-crowd-counting","title":"Mask-aware networks for crowd counting","date":"2018-12-18","arxiv_id":"1901.00039","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-semi-supervised-generative","title":"Generalizing semi-supervised generative adversarial networks to regression using feature contrasting","date":"2018-11-27","arxiv_id":"1811.11269","repositories_listed":0,"syntology":null},{"url":null,"slug":"padnet-pan-density-crowd-counting","title":"PaDNet: Pan-Density Crowd Counting","date":"2018-11-07","arxiv_id":"1811.02805","repositories_listed":0,"syntology":null},{"url":null,"slug":"in-defense-of-single-column-networks-for","title":"In Defense of Single-column Networks for Crowd Counting","date":"2018-08-18","arxiv_id":"1808.06133","repositories_listed":0,"syntology":null},{"url":"/paper/composition-loss-for-counting-density-map","slug":"composition-loss-for-counting-density-map","title":"Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds","date":"2018-08-02","arxiv_id":"1808.01050","repositories_listed":0,"syntology":null},{"url":"/paper/divide-and-grow-capturing-huge-diversity-in","slug":"divide-and-grow-capturing-huge-diversity-in","title":"Divide and Grow: Capturing Huge Diversity in Crowd Images with Incrementally Growing CNN","date":"2018-07-26","arxiv_id":"1807.09993","repositories_listed":0,"syntology":null},{"url":"/paper/iterative-crowd-counting","slug":"iterative-crowd-counting","title":"Iterative Crowd Counting","date":"2018-07-26","arxiv_id":"1807.09959","repositories_listed":0,"syntology":null},{"url":null,"slug":"top-down-feedback-for-crowd-counting","title":"Top-Down Feedback for Crowd Counting Convolutional Neural Network","date":"2018-07-24","arxiv_id":"1807.08881","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-perspective-information-for","title":"Revisiting Perspective Information for Efficient Crowd Counting","date":"2018-07-05","arxiv_id":"1807.01989","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-using-deep-recurrent-spatial","title":"Crowd Counting using Deep Recurrent Spatial-Aware Network","date":"2018-07-02","arxiv_id":"1807.00601","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-to-head-locations-for-crowd","title":"Attention to Head Locations for Crowd Counting","date":"2018-06-27","arxiv_id":"1806.10287","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-with-density-adaption-networks","title":"Crowd Counting with Density Adaption Networks","date":"2018-06-26","arxiv_id":"1806.10040","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-counting-with-small-datasets-of-large","title":"Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images","date":"2018-05-28","arxiv_id":"1805.11123","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-by-adaptively-fusing","title":"Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid","date":"2018-05-16","arxiv_id":"1805.06115","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deeply-recursive-convolutional-network-for","title":"A Deeply-Recursive Convolutional Network for Crowd Counting","date":"2018-05-15","arxiv_id":"1805.05633","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-ccnn-adaptive-ccnn-for-density-estimation","title":"A-CCNN: adaptive ccnn for density estimation and crowd counting","date":"2018-04-19","arxiv_id":"1804.06958","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-information-guided-crowd-counting-for","title":"Depth Information Guided Crowd Counting for Complex Crowd Scenes","date":"2018-03-03","arxiv_id":"1803.02256","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-inhomogeneous-density-map-learning","title":"Structured Inhomogeneous Density Map Learning for Crowd Counting","date":"2018-01-20","arxiv_id":"1801.06642","repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-side-information-by-adaptive","title":"Incorporating Side Information by Adaptive Convolution","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-through-walls-using-wifi","title":"Crowd Counting Through Walls Using WiFi","date":"2017-11-15","arxiv_id":"1711.05837","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-spatial-regression-model-for-image-crowd","title":"Deep Spatial Regression Model for Image Crowd Counting","date":"2017-10-26","arxiv_id":"1710.09757","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-dedicated-computer-vision-tool-set","title":"Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models","date":"2017-09-01","arxiv_id":"1709.02243","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-perspective-embedded-deconvolution","title":"Learning a perspective-embedded deconvolution network for crowd counting","date":"2017-08-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/generating-high-quality-crowd-density-maps","slug":"generating-high-quality-crowd-density-maps","title":"Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs","date":"2017-08-02","arxiv_id":"1708.00953","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-modeling-for-crowd-counting-in","title":"Spatiotemporal Modeling for Crowd Counting in Videos","date":"2017-07-25","arxiv_id":"1707.07890","repositories_listed":0,"syntology":null},{"url":null,"slug":"soft-margin-mixture-of-regressions","title":"Soft-Margin Mixture of Regressions","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-of-counting-cnns-adaptive-integration","title":"Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting","date":"2017-03-28","arxiv_id":"1703.09393","repositories_listed":0,"syntology":null},{"url":null,"slug":"fully-convolutional-crowd-counting-on-highly","title":"Fully Convolutional Crowd Counting On Highly Congested Scenes","date":"2016-12-01","arxiv_id":"1612.00220","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-by-adapting-convolutional","title":"Crowd Counting by Adapting Convolutional Neural Networks with Side Information","date":"2016-11-21","arxiv_id":"1611.06748","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-considering-network-flow","title":"Crowd Counting Considering Network Flow Constraints in Videos","date":"2016-05-12","arxiv_id":"1605.03821","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowd-counting-via-weighted-vlad-on-dense","title":"Crowd Counting via Weighted VLAD on Dense Attribute Feature Maps","date":"2016-04-29","arxiv_id":"1604.08660","repositories_listed":0,"syntology":null},{"url":null,"slug":"scene-invariant-crowd-segmentation-and","title":"Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)","date":"2016-02-01","arxiv_id":"1602.00386","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-model-adaptation-for-crowd-counts","title":"Bayesian Model Adaptation for Crowd Counts","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"people-counting-in-high-density-crowds-from","title":"People Counting in High Density Crowds from Still Images","date":"2015-07-30","arxiv_id":"1507.08445","repositories_listed":0,"syntology":null},{"url":"/paper/cross-scene-crowd-counting-via-deep","slug":"cross-scene-crowd-counting-via-deep","title":"Cross-Scene Crowd Counting via Deep Convolutional Neural Networks","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parametric-regression-on-the-grassmannian","title":"Parametric Regression on the Grassmannian","date":"2015-05-14","arxiv_id":"1505.03832","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossing-the-line-crowd-counting-by-integer","title":"Crossing the Line: Crowd Counting by Integer Programming with Local Features","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cumulative-attribute-space-for-age-and-crowd","title":"Cumulative Attribute Space for Age and Crowd Density Estimation","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/multi-source-multi-scale-counting-in","slug":"multi-source-multi-scale-counting-in","title":"Multi-source Multi-scale Counting in Extremely Dense Crowd Images","date":"2013-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"ea7c051b5df1c234f0d2a2d3db37aa2691ce9a53dd3e4f13a3c2fe5627dd4a43","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}