{"task":"Interactive Segmentation","dataset":"Berkeley","metric_names":["NoC@90","NoC@95"],"rows":[{"id":88374,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"ICL CFR-1 (ViT-H, C+L)","metrics":{"NoC@90":"1.46","NoC@95":"2.90"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88375,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"FocalClick-B3-S2","metrics":{"NoC@90":"1.48"},"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@95\"]","area":"Computer Vision","uses_additional_data":1,"source":"archive","tags":[]},{"id":88376,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"ViT-B+MST+CL","metrics":{"NoC@90":"1.50"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88377,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"SimpleClick (ViT-H, C+L)","metrics":{"NoC@90":"1.75"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88378,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"SimpleClick (ViT-H, SBD)","metrics":{"NoC@90":"2.09"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88379,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"IA-FP-Net(HRNet, C+L)","metrics":{"NoC@90":"2.12"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88380,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"RITM (HRNet18, C+L)","metrics":{"NoC@90":"2.26"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88381,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"EdgeFlow","metrics":{"NoC@90":"2.4"},"paper_url":"https://arxiv.org/abs/2109.09406v2","paper_title":"EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow","paper_date":"2021-09-20","code_links":[{"title":"PaddlePaddle/PaddleSeg","url":"https://github.com/PaddlePaddle/PaddleSeg"},{"title":"PaddleCV-SIG/EISeg","url":"https://github.com/PaddleCV-SIG/EISeg"},{"title":"ihomeava/PaddleSeg2","url":"https://github.com/ihomeava/PaddleSeg2"}],"metrics_order":"[\"NoC@90\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88382,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"UCP-Net","metrics":{"NoC@90":"2.70"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88383,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"RITM (HRNet18, SBD)","metrics":{"NoC@90":"3.22"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88384,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"f-BRS-B (ResNet-50)","metrics":{"NoC@90":"4.34"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88385,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"IA+SA","metrics":{"NoC@90":"4.94"},"paper_url":"https://arxiv.org/abs/1911.12709v4","paper_title":"Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections","paper_date":"2019-11-28","code_links":[],"metrics_order":"[\"NoC@90\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88386,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"BRS","metrics":{"NoC@90":"5.08"},"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@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]},{"id":88387,"task":"Interactive Segmentation","parent_task":null,"dataset":"Berkeley","model_name":"CM guidance","metrics":{"NoC@90":"5.60"},"paper_url":"http://openaccess.thecvf.com/content_CVPR_2019/html/Majumder_Content-Aware_Multi-Level_Guidance_for_Interactive_Instance_Segmentation_CVPR_2019_paper.html","paper_title":"Content-Aware Multi-Level Guidance for Interactive Instance Segmentation","paper_date":"2019-06-01","code_links":[],"metrics_order":"[\"NoC@90\", \"NoC@95\"]","area":"Computer Vision","uses_additional_data":0,"source":"archive","tags":[]}]}