{"task":"Interactive Segmentation","dataset":"SBD","metric_names":["NoC@90","NoC@85","NoC@95"],"rows":[{"id":88409,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"SimpleClick CFR-1 (ViT-H, SBD)","metrics":{"NoC@85":"2.45","NoC@90":"4.08","NoC@95":"9.80"},"paper_url":"https://arxiv.org/abs/2303.05620v2","paper_title":"CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation","paper_date":"2023-03-09","code_links":[{"title":"TitorX/CFR-ICL-Interactive-Segmentation","url":"https://github.com/TitorX/CFR-ICL-Interactive-Segmentation"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88410,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"SimpleClick","metrics":{"NoC@85":"2.51","NoC@90":"4.15"},"paper_url":"https://arxiv.org/abs/2210.11006v3","paper_title":"SimpleClick: Interactive Image Segmentation with Simple Vision Transformers","paper_date":"2022-10-20","code_links":[{"title":"uncbiag/simpleclick","url":"https://github.com/uncbiag/simpleclick"},{"title":"yihanhu-2022/diffmatte","url":"https://github.com/yihanhu-2022/diffmatte"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88411,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"UCP-Net","metrics":{"NoC@85":"2.73","NoC@90":"5.00"},"paper_url":"https://arxiv.org/abs/2109.07592v1","paper_title":"UCP-Net: Unstructured Contour Points for Instance Segmentation","paper_date":"2021-09-15","code_links":[],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88412,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"IA-FP-Net(HRNet,SBD)","metrics":{"NoC@85":"3.33","NoC@90":"5.25"},"paper_url":"https://arxiv.org/abs/2203.05145v3","paper_title":"Cascaded Sparse Feature Propagation Network for Interactive Segmentation","paper_date":"2022-03-10","code_links":[{"title":"kleinzcy/csfpn","url":"https://github.com/kleinzcy/csfpn"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88413,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"FocusCut","metrics":{"NoC@85":"3.40","NoC@90":"5.31"},"paper_url":"http://openaccess.thecvf.com//content/CVPR2022/html/Lin_FocusCut_Diving_Into_a_Focus_View_in_Interactive_Segmentation_CVPR_2022_paper.html","paper_title":"FocusCut: Diving Into a Focus View in Interactive Segmentation","paper_date":"2022-01-01","code_links":[{"title":"frazerlin/focuscut","url":"https://github.com/frazerlin/focuscut"},{"title":"Dragoniss/mindspore-phase2-FocusCut","url":"https://github.com/Dragoniss/mindspore-phase2-FocusCut/blob/main/FocusCut.py"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88414,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"RITM (HRNet18, SBD)","metrics":{"NoC@85":"3.39","NoC@90":"5.43"},"paper_url":"https://arxiv.org/abs/2102.06583v1","paper_title":"Reviving Iterative Training with Mask Guidance for Interactive Segmentation","paper_date":"2021-02-12","code_links":[{"title":"PaddlePaddle/PaddleSeg","url":"https://github.com/PaddlePaddle/PaddleSeg"},{"title":"mazurowski-lab/segment-anything-medical-evaluation","url":"https://github.com/mazurowski-lab/segment-anything-medical-evaluation"},{"title":"mazurowski-lab/segment-anything-medical","url":"https://github.com/mazurowski-lab/segment-anything-medical"},{"title":"supervisely-ecosystem/ritm-interactive-segmentation","url":"https://github.com/supervisely-ecosystem/ritm-interactive-segmentation"},{"title":"supervisely-ecosystem/ritm_interactive_segmentation","url":"https://github.com/supervisely-ecosystem/ritm_interactive_segmentation"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88415,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"FocalClick","metrics":{"NoC@85":"3.53","NoC@90":"5.59"},"paper_url":"https://arxiv.org/abs/2204.02574v2","paper_title":"FocalClick: Towards Practical Interactive Image Segmentation","paper_date":"2022-04-06","code_links":[{"title":"XavierCHEN34/ClickSEG","url":"https://github.com/XavierCHEN34/ClickSEG"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88416,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"f-BRS-B (ResNet-101)","metrics":{"NoC@85":"4.81","NoC@90":"7.73"},"paper_url":"https://arxiv.org/abs/2001.10331v3","paper_title":"f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation","paper_date":"2020-01-28","code_links":[{"title":"PaddlePaddle/PaddleSeg","url":"https://github.com/PaddlePaddle/PaddleSeg"},{"title":"jpconnel/fbrs-segmentation","url":"https://github.com/jpconnel/fbrs-segmentation"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88417,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"BRS","metrics":{"NoC@85":"6.59","NoC@90":"9.78"},"paper_url":"http://openaccess.thecvf.com/content_CVPR_2019/html/Jang_Interactive_Image_Segmentation_via_Backpropagating_Refinement_Scheme_CVPR_2019_paper.html","paper_title":"Interactive Image Segmentation via Backpropagating Refinement Scheme","paper_date":"2019-06-01","code_links":[],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88418,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"Latent diversity","metrics":{"NoC@85":"7.41","NoC@90":"10.78"},"paper_url":"http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Interactive_Image_Segmentation_CVPR_2018_paper.html","paper_title":"Interactive Image Segmentation With Latent Diversity","paper_date":"2018-06-01","code_links":[{"title":"intel-isl/Intseg","url":"https://github.com/intel-isl/Intseg"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88419,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"DOS with GC","metrics":{"NoC@85":"9.22","NoC@90":"12.80"},"paper_url":"http://arxiv.org/abs/1603.04042v1","paper_title":"Deep Interactive Object Selection","paper_date":"2016-03-13","code_links":[{"title":"intel-isl/Intseg","url":"https://github.com/intel-isl/Intseg"},{"title":"IntelVCL/Intseg","url":"https://github.com/IntelVCL/Intseg"},{"title":"KanchanIIT/Highly-Optimized-Interactive-Image-Segmentation","url":"https://github.com/KanchanIIT/Highly-Optimized-Interactive-Image-Segmentation"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88420,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"DOS w/o GC","metrics":{"NoC@85":"14.30","NoC@90":"16.79"},"paper_url":"http://arxiv.org/abs/1603.04042v1","paper_title":"Deep Interactive Object Selection","paper_date":"2016-03-13","code_links":[{"title":"intel-isl/Intseg","url":"https://github.com/intel-isl/Intseg"},{"title":"IntelVCL/Intseg","url":"https://github.com/IntelVCL/Intseg"},{"title":"KanchanIIT/Highly-Optimized-Interactive-Image-Segmentation","url":"https://github.com/KanchanIIT/Highly-Optimized-Interactive-Image-Segmentation"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88421,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"ViT-B+MST+CL","metrics":{"NoC@85":"3.03"},"paper_url":"https://arxiv.org/abs/2401.04403v2","paper_title":"MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation","paper_date":"2024-01-09","code_links":[{"title":"hahamyt/mst","url":"https://github.com/hahamyt/mst"}],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88422,"task":"Interactive Segmentation","parent_task":null,"dataset":"SBD","model_name":"RIS-Net","metrics":{"NoC@85":"6.03"},"paper_url":"http://openaccess.thecvf.com/content_iccv_2017/html/Liew_Regional_Interactive_Image_ICCV_2017_paper.html","paper_title":"Regional Interactive Image Segmentation Networks","paper_date":"2017-10-01","code_links":[],"metrics_order":"[\"NoC@90\", \"NoC@85\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]}]}