Regularizing Vector Embedding in Bottom-Up Human Pose Estimation
文献类型：会议论文
| 作者 | Wang Haixin2,4 ; Zhou Lu2; Chen Yingying2; Tang Ming2,4; Wang Jinqiao1,2,3,4
|
| 出版日期 | 2022-10 |
| 会议日期 | October 23-27, 2022 |
| 会议地点 | Tel Aviv, Israel |
| 英文摘要 | The embedding-based method such as Associative Embed
ding is popular in bottom-up human pose estimation. Methods under
this framework group candidate keypoints according to the predicted
identity embeddings. However, the identity embeddings of different in
stances are likely to be linearly inseparable in some complex scenes, such
as crowded scene or when the number of instances in the image is large.
To reduce the impact of this phenomenon on keypoint grouping, we try
to learn a sparse multidimensional embedding for each keypoint. We
observe that the different dimensions of embeddings are highly linearly
correlated. To address this issue, we impose an additional constraint on
the embeddings during training phase. Based on the fact that the scales
of instances usually have significant variations, we uilize the scales of in
stances to regularize the embeddings, which effectively reduces the linear
correlation of embeddings and makes embeddings being sparse. We eval
uate our model on CrowdPose Test and COCO Test-dev. Compared to
vanilla Associative Embedding, our method has an impressive superiority
in keypoint grouping, especially in crowded scenes with a large number
of instances. Furthermore, our method achieves state-of-the-art results
on CrowdPose Test (74.5 AP) and COCO Test-dev (72.8 AP), outper
forming other bottom-up methods. Our code and pretrained models are
available at https://github.com/CR320/CoupledEmbedding. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57168]  |
| 专题 | 紫东太初大模型研究中心 |
| 作者单位 | 1.ObjectEye Inc 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 3.Peng Cheng Laboratory 4.School of Artificial Intelligence, University of Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Wang Haixin,Zhou Lu,Chen Yingying,et al. Regularizing Vector Embedding in Bottom-Up Human Pose Estimation[C]. 见:. Tel Aviv, Israel. October 23-27, 2022. |
入库方式： OAI收割
来源：自动化研究所
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