paper-with-me

Pose Estimation 벤치마크

Pose Estimation on MPII Human Pose

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PCKh-0.5

80.9 84.25 87.6 90.95 94.3 2014-11 2026-09 Tompson et al. — 82.0 (2014-11-16) Tompson et al. — 82.0 (2014-11-16) Tompson et al. — 82.0 (2014-11-16) QP2 — 82.4 (2015-07-21) QP1 — 81.1 (2015-07-21) QP2 — 82.4 (2015-07-21) QP1 — 81.1 (2015-07-21) QP2 — 82.4 (2015-07-21) QP1 — 81.1 (2015-07-21) IEF — 81.3 (2015-07-23) IEF — 81.3 (2015-07-23) IEF — 81.3 (2015-07-23) DeepCut — 82.4 (2015-11-20) DeepCut — 82.4 (2015-11-20) DeepCut — 82.4 (2015-11-20) Convolutional Pose Machines — 88.52 (2016-01-30) Convolutional Pose Machines — 88.52 (2016-01-30) Convolutional Pose Machines — 88.52 (2016-01-30) Stacked Hourglass Networks — 90.9 (2016-03-22) Stacked Hourglass Networks — 90.9 (2016-03-22) Stacked Hourglass Networks — 90.9 (2016-03-22) Lifshitz et al. — 85.0 (2016-03-27) Lifshitz et al. — 85.0 (2016-03-27) Lifshitz et al. — 85.0 (2016-03-27) ResNet-152 + intermediate supervision — 88.52 (2016-05-10) ResNet-152 + intermediate supervision — 88.52 (2016-05-10) ResNet-152 + intermediate supervision — 88.52 (2016-05-10) Human Pose Estimation — 89.7 (2016-09-06) Part heatmap regression (ResNet-152) — 89.7 (2016-09-06) Human Pose Estimation — 89.7 (2016-09-06) Part heatmap regression (ResNet-152) — 89.7 (2016-09-06) Human Pose Estimation — 89.7 (2016-09-06) Part heatmap regression (ResNet-152) — 89.7 (2016-09-06) Multi-Context Attention — 91.5 (2017-02-24) Multi-Context Attention — 91.5 (2017-02-24) Multi-Context Attention — 91.5 (2017-02-24) CHPR — 86.4 (2017-04-01) CHPR — 86.4 (2017-04-01) CHPR — 86.4 (2017-04-01) Chen et al. ICCV'17 — 91.9 (2017-04-30) Chen et al. ICCV'17 — 91.9 (2017-04-30) Chen et al. ICCV'17 — 91.9 (2017-04-30) Stacked hourglass + Inception-resnet — 91.2 (2017-05-05) Stacked hourglass + Inception-resnet — 91.2 (2017-05-05) Stacked hourglass + Inception-resnet — 91.2 (2017-05-05) Chou et al. arXiv'17 — 91.8 (2017-07-08) Chou et al. arXiv'17 — 91.8 (2017-07-08) Chou et al. arXiv'17 — 91.8 (2017-07-08) Pyramid Residual Modules (PRMs) — 92.0 (2017-08-03) Pyramid Residual Modules (PRMs) — 92.0 (2017-08-03) Pyramid Residual Modules (PRMs) — 92.0 (2017-08-03) Integral Regression — 91.0 (2017-11-22) Integral Regression — 91.0 (2017-11-22) Integral Regression — 91.0 (2017-11-22) DSNTr ResNet-50@28px — 89.5 (2018-01-23) DSNTr ResNet-50@28px — 89.5 (2018-01-23) DSNTr ResNet-50@28px — 89.5 (2018-01-23) Multi-Scale Structure-Aware Network — 92.1 (2018-03-27) Multi-Scale Structure-Aware Network — 92.1 (2018-03-27) Multi-Scale Structure-Aware Network — 92.1 (2018-03-27) Residual Hourglass +ASR+AHO — 91.5 (2018-05-24) Residual Hourglass +ASR+AHO — 91.5 (2018-05-24) Residual Hourglass +ASR+AHO — 91.5 (2018-05-24) DU-Net — 91.2 (2018-08-07) DU-Net — 91.2 (2018-08-07) DU-Net — 91.2 (2018-08-07) x — 81.3 (2018-08-14) x — 81.3 (2018-08-14) x — 81.3 (2018-08-14) CU-Net — 89.4 (2018-08-20) CU-Net — 89.4 (2018-08-20) CU-Net — 89.4 (2018-08-20) DLCM — 92.3 (2018-09-01) DLCM — 92.3 (2018-09-01) DLCM — 92.3 (2018-09-01) FPD — 91.1 (2018-11-13) FPD — 91.1 (2018-11-13) FPD — 91.1 (2018-11-13) MSPN — 92.6 (2019-01-01) MSPN — 92.6 (2019-01-01) MSPN — 92.6 (2019-01-01) Spatial Context — 92.5 (2019-01-07) Spatial Context — 92.5 (2019-01-07) Spatial Context — 92.5 (2019-01-07) Cascade Feature Aggregation — 93.9 (2019-02-21) Cascade Feature Aggregation — 93.9 (2019-02-21) Cascade Feature Aggregation — 93.9 (2019-02-21) HRNet-W32 — 92.3 (2019-02-25) HRNet-W32 — 92.3 (2019-02-25) HRNet-W32 — 92.3 (2019-02-25) Tucker T-Net — 87.5 (2019-04-04) Tucker T-Net — 87.5 (2019-04-04) Tucker T-Net — 87.5 (2019-04-04) Improved Binary Network (HourGlass) — 80.9 (2019-04-11) Improved Binary Network (HourGlass) — 80.9 (2019-04-11) Improved Binary Network (HourGlass) — 80.9 (2019-04-11) Matrix — 82.5 (2019-04-16) Matrix — 82.5 (2019-04-16) Matrix — 82.5 (2019-04-16) DarkPose — 90.6 (2019-10-14) DarkPose — 90.6 (2019-10-14) DarkPose — 90.6 (2019-10-14) UniPose — 92.7 (2020-01-22) UniPose — 92.7 (2020-01-22) UniPose — 92.7 (2020-01-22) Soft-gated Skip Connections — 94.1 (2020-02-25) Soft-gated Skip Connections — 94.1 (2020-02-25) Soft-gated Skip Connections — 94.1 (2020-02-25) 4xRSN-50 — 93.0 (2020-03-09) 4xRSN-50 — 93.0 (2020-03-09) 4xRSN-50 — 93.0 (2020-03-09) EfficientPose IV — 91.2 (2020-04-25) OpenPose — 88.8 (2020-04-25) EfficientPose RT — 84.8 (2020-04-25) EfficientPose IV — 91.2 (2020-04-25) OpenPose — 88.8 (2020-04-25) EfficientPose RT — 84.8 (2020-04-25) EfficientPose IV — 91.2 (2020-04-25) OpenPose — 88.8 (2020-04-25) EfficientPose RT — 84.8 (2020-04-25) TransPose — 93.5 (2020-12-28) TransPose — 93.5 (2020-12-28) TransPose — 93.5 (2020-12-28) TFPose(ResNet-50) — 90.4 (2021-03-29) TFPose(ResNet-50) — 90.4 (2021-03-29) TFPose(ResNet-50) — 90.4 (2021-03-29) Dite-HRNet-30 — 87.6 (2022-04-22) Dite-HRNet-30 — 87.6 (2022-04-22) Dite-HRNet-30 — 87.6 (2022-04-22) UniHCP (FT) — 93.2 (2023-03-06) UniHCP (FT) — 93.2 (2023-03-06) UniHCP (FT) — 93.2 (2023-03-06) PCT (swin-l, test set) — 94.3 (2023-03-21) PCT (swin-b, test set) — 93.8 (2023-03-21) PCT (swin-l, test set) — 94.3 (2023-03-21) PCT (swin-b, test set) — 93.8 (2023-03-21) PCT (swin-l, test set) — 94.3 (2023-03-21) PCT (swin-b, test set) — 93.8 (2023-03-21) Tompson et al. — 82.0 (2014-11-16) QP2 — 82.4 (2015-07-21) Convolutional Pose Machines — 88.52 (2016-01-30) Stacked Hourglass Networks — 90.9 (2016-03-22) Multi-Context Attention — 91.5 (2017-02-24) Chen et al. ICCV'17 — 91.9 (2017-04-30) Pyramid Residual Modules (PRMs) — 92.0 (2017-08-03) Multi-Scale Structure-Aware Network — 92.1 (2018-03-27) DLCM — 92.3 (2018-09-01) MSPN — 92.6 (2019-01-01) Cascade Feature Aggregation — 93.9 (2019-02-21) Soft-gated Skip Connections — 94.1 (2020-02-25) PCT (swin-l, test set) — 94.3 (2023-03-21)
RankModel PCKh-0.5 Extra Training Data PaperCodeYear
101 MSPN 92.6 Rethinking on Multi-Stage Networks for Human Pose Estimation open-mmlab/mmpose · chenyilun95/tf-cpn · megvii-detection/MSPN · +4 2019
102 Spatial Context 92.5 Human Pose Estimation with Spatial Contextual Information 2019
103 HRNet-W32 92.3 Deep High-Resolution Representation Learning for Human Pose Estimation open-mmlab/mmdetection · PaddlePaddle/PaddleDetection · open-mmlab/mmpose · +36 2019
103 DLCM 92.3 Deeply Learned Compositional Models for Human Pose Estimation 2018
105 Multi-Scale Structure-Aware Network 92.1 Multi-Scale Structure-Aware Network for Human Pose Estimation 2018
106 Pyramid Residual Modules (PRMs) 92.0 Learning Feature Pyramids for Human Pose Estimation bearpaw/PyraNet · wanggrun/Learning-Feature-Pyramids · wanggrun/Learning-Feature-Pyramids-For-COCO 2017
107 Chen et al. ICCV'17 91.9 Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation rohitrango/Adversarial-Pose-Estimation · Mind23-2/MindCode-5 2017
108 Chou et al. arXiv'17 91.8 Self Adversarial Training for Human Pose Estimation dongzhuoyao/jessiechouuu-adversarial-pose 2017
109 Multi-Context Attention 91.5 Multi-Context Attention for Human Pose Estimation wbenbihi/hourglasstensorlfow · bearpaw/pose-attention 2017
109 Residual Hourglass +ASR+AHO 91.5 Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation 2018
111 DU-Net 91.2 Quantized Densely Connected U-Nets for Efficient Landmark Localization zhiqiangdon/CU-Net 2018
111 Stacked hourglass + Inception-resnet 91.2 Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation Guanghan/GNet-pose 2017
111 EfficientPose IV 91.2 EfficientPose: Scalable single-person pose estimation daniegr/EfficientPose 2020
114 FPD 91.1 Fast Human Pose Estimation ilovepose/fast-human-pose-estimation.pytorch 2018
115 Integral Regression 91.0 Integral Human Pose Regression JimmySuen/integral-human-pose · strawberryfg/c2f-3dhm-human-caffe 2017
116 Stacked Hourglass Networks 90.9 Stacked Hourglass Networks for Human Pose Estimation open-mmlab/mmpose · bearpaw/pytorch-pose · MandyMo/pytorch_HMR · +43 2016
117 DarkPose 90.6 Distribution-Aware Coordinate Representation for Human Pose Estimation PaddlePaddle/PaddleDetection · open-mmlab/mmpose · leoxiaobin/deep-high-resolution-net.pytorch · +3 2019
118 TFPose(ResNet-50) 90.4 TFPose: Direct Human Pose Estimation with Transformers 2021
119 Human Pose Estimation 89.7 Human pose estimation via Convolutional Part Heatmap Regression 1adrianb/human-pose-estimation 2016
119 Part heatmap regression (ResNet-152) 89.7 Human pose estimation via Convolutional Part Heatmap Regression 1adrianb/human-pose-estimation 2016
121 DSNTr ResNet-50@28px 89.5 Numerical Coordinate Regression with Convolutional Neural Networks anibali/dsntnn · mansimane/WormML 2018
122 CU-Net 89.4 CU-Net: Coupled U-Nets zhiqiangdon/CU-Net 2018
123 OpenPose 88.8 EfficientPose: Scalable single-person pose estimation daniegr/EfficientPose 2020
124 Convolutional Pose Machines 88.52 Convolutional Pose Machines CMU-Perceptual-Computing-Lab/openpose · open-mmlab/mmpose · shihenw/convolutional-pose-machines-release · +47 2016
124 ResNet-152 + intermediate supervision 88.52 DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model eldar/pose-tensorflow · eldar/deepcut · yttrilab/b-soid · +13 2016
126 Dite-HRNet-30 87.6 Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation ziyizhang27/dite-hrnet 2022
127 Tucker T-Net 87.5 T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor 2019
128 CHPR 86.4 Compositional Human Pose Regression anibali/h36m-fetch 2017
129 Lifshitz et al. 85.0 Human Pose Estimation using Deep Consensus Voting 2016
130 EfficientPose RT 84.8 EfficientPose: Scalable single-person pose estimation daniegr/EfficientPose 2020
131 Matrix 82.5 Matrix and tensor decompositions for training binary neural networks 2019
132 DeepCut 82.40 DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation eldar/deepcut · eldar/deepcut-cnn · gsoykan/deepercut-replication · +1 2015
132 QP2 82.4 Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians peiyunh/rg-mpii 2015
134 Tompson et al. 82.0 Efficient Object Localization Using Convolutional Networks cmu-enyac/Renofeation · yukitsuji/chainer_function 2014
135 x 81.3 Hierarchical binary CNNs for landmark localization with limited resources 1adrianb/binary-networks-pytorch 2018
135 IEF 81.3 Human Pose Estimation with Iterative Error Feedback pulkitag/ief 2015
137 QP1 81.1 Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians peiyunh/rg-mpii 2015
138 Improved Binary Network (HourGlass) 80.9 Improved training of binary networks for human pose estimation and image recognition 1adrianb/binary-networks-pytorch 2019
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