EFCPose: End-to-End Multi-Person Pose Estimation with Fully Convolutional Heads
文献类型：期刊论文
| 作者 | Wang Haixin2,3 ; Zhou Lu3; Chen Yingying3; Wang Jinqiao1,2,3,4
|
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2023 |
| 页码 | early access |
| 英文摘要 | Mainstream methods of multi-person pose estimation are not end-to-end. Recently, some methods build an end-to-end framework based on the DETR framework, aiming
to eliminate the need for hand-crafted modules like heuristic
grouping and NMS post-processing. However, these DETR-based
methods suffer from a heavy memory burden of processing the
high-resolution backbone feature maps with transformers. In this
paper, we propose an end-to-end multi-person pose estimation
method with a fully convolutional network, termed EFCPose. Different from DETR-based methods, it directly predicts instance-aware poses in a pixel-wise manner with lightweight convolutional
heads, avoiding the heavy memory burden. Overall, our method
adopts the center-offset formulation and a one-to-one label
assignment strategy to achieve the multi-person pose estimation
in an end-to-end manner. The main contribution of our fully
convolutional heads includes two aspects. On the one hand, we
propose an unaligned center-offset representation to learn more
reliable semantic centers to replace the inconsistent geometric
centers, improving the performance of instance detection. On the
other hand, we propose a novel regression strategy named limb-aware adaptive regression, which leverages separate adaptive
points to convert challenging long-range offsets into simplified
short-range offsets and incorporates limb constraints to elevate
the regression quality of joint offsets. Compared with current
DETR-based end-to-end methods, EFCPose avoids high com
putational complexity and achieves higher accuracy. Extensive
experiments on COCO Keypoint and CrowdPose benchmarks
show that EFCPose outperforms other state-of-the-art bottom
up and single-stage methods without flipping augmentation. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57152]  |
| 专题 | 紫东太初大模型研究中心 |
| 通讯作者 | Wang Haixin; Chen Yingying |
| 作者单位 | 1.Wuhan AI Research 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences 4.Peng Cheng Laboratory |
| 推荐引用方式 GB/T 7714 | Wang Haixin,Zhou Lu,Chen Yingying,et al. EFCPose: End-to-End Multi-Person Pose Estimation with Fully Convolutional Heads[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023:early access. |
| APA | Wang Haixin,Zhou Lu,Chen Yingying,&Wang Jinqiao.(2023).EFCPose: End-to-End Multi-Person Pose Estimation with Fully Convolutional Heads.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,early access. |
| MLA | Wang Haixin,et al."EFCPose: End-to-End Multi-Person Pose Estimation with Fully Convolutional Heads".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2023):early access. |
入库方式： OAI收割
来源：自动化研究所
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