[ { "MAE": 58.3, "code_links": [ { "title": "xhp-hust-2018-2011/S-DCNet", "url": "https://github.com/xhp-hust-2018-2011/S-DCNet" }, { "title": "MohamedAliRashad/Crowd-DCNet", "url": "https://github.com/MohamedAliRashad/Crowd-DCNet" } ], "date": "2019-08-15", "date2": 20190815, "model": "S-DCNet", "paper": { "title": "From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer", "url": "https://cknow.io/lib/534e840428a0087b" }, "paper_data_uoa": "534e840428a0087b" }, { "MAE": 66.4, "code_links": [ { "title": "val-iisc/lsc-cnn", "url": "https://github.com/val-iisc/lsc-cnn" } ], "date": "2019-06-18", "date2": 20190618, "model": "LSC-CNN", "paper": { "title": "Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection", "url": "https://cknow.io/lib/133f82d94a848439" }, "paper_data_uoa": "133f82d94a848439" }, { "MAE": 101.3, "code_links": [ { "title": "svishwa/crowdcount-cascaded-mtl", "url": "https://github.com/svishwa/crowdcount-cascaded-mtl" }, { "title": "surajdakua/Crowd-Counting-Using-Pytorch", "url": "https://github.com/surajdakua/Crowd-Counting-Using-Pytorch" } ], "date": "2017-07-30", "date2": 20170730, "model": "Cascaded-MTL", "paper": { "title": "CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting", "url": "https://cknow.io/lib/40f291ffa4d2b454" }, "paper_data_uoa": "40f291ffa4d2b454" } ]