Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation
這篇文章不是介紹怎麼設計網絡去提高多人姿態檢測在數據集上的mAP值，而是去找出姿態估計算法爲啥得分這麼低的原因，更直白的講就是，這個模型效果不好，這個效果不好具體是哪些地方效果不好，文章探究的是這個更底層的原因，而不是簡單粗暴的堆trick提高mAP。文章是2017年發表的，當時比較出名的兩個多人人體姿態估計網絡是Google的GRMI和CMU的OpenPose，文章就是根據這兩個模型來查找原因。
相關文章
- 1. UniPose: Unified Human Pose Estimation in Single Images and Videos
- 2. 3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
- 3. Human Pose Estimation with Iterative Error Feedback
- 4. 【轉】DensePose: Dense Human Pose Estimation In The Wild
- 5. Deep Learning Models and Code for Pose Estimation
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- 7. 3D human pose estimation in video with temporal convolutions and semi-supervised training
- 8. 姿態估計及跟蹤「Detect-and-Track: Efficient Pose Estimation in Videos」
- 9. 2020.09.22讀 PointVoteNet：accurate object detection and 6 DoF pose estimation in point clouds
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相關文章
- 1. UniPose: Unified Human Pose Estimation in Single Images and Videos
- 2. 3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
- 3. Human Pose Estimation with Iterative Error Feedback
- 4. 【轉】DensePose: Dense Human Pose Estimation In The Wild
- 5. Deep Learning Models and Code for Pose Estimation
- 6. DensePose: Dense Human Pose Estimation In The Wild
- 7. 3D human pose estimation in video with temporal convolutions and semi-supervised training
- 8. 姿態估計及跟蹤「Detect-and-Track: Efficient Pose Estimation in Videos」
- 9. 2020.09.22讀 PointVoteNet：accurate object detection and 6 DoF pose estimation in point clouds
- 10. 2D Pose Estimation