.png){kind=icon}
返回
HRNeXt: High-Resolution Context Network for Crowd Pose Estimation
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2023.3248144.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Occlusion handling in crowded scenes is an intractable challenge for human pose estimation. To address this problem, we propose two novel feed-forward network structures named Global Feed-Forward Network (GFFN) and Dynamic Feed-Forward Network (DFFN), which are specifically designed for image-based tasks to capture both local and global contextual information within intermediate features and update feature representations with high adaptability for occlusions. By exploiting the context modeling ability of the proposed GFFN and DFFN, we present a novel backbone network, namely High-Resolution Context Network (HRNeXt), which learns high-resolution representations with abundant contextual information to better estimate poses of occluded human bodies. Compared to state-of-the-art pose estimation networks, our HRNeXt absorbs advantages of convolution operation and attention mechanism, and it is more efficient in terms of training data sizes, network parameters and computational costs. Experimental results show that our HRNeXt significantly outperforms state-of-the-art backbone networks on challenging pose estimation datasets with high occurrence of crowds and occlusions.
Keyword:
Pose estimation
Convolution
Task analysis
Kernel
Feature extraction
Context modeling
Transformers
Crowd pose estimation
context modeling
feed-forward network
attention mechanism
high-resolution representation
期刊
IF:
9.7
论文数:
4.7K
被引数:
2.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten?能力的感知是否可以介导幼儿园儿童的基本运动技能熟练程度与身体活动水平之间的关系？
PREAI
分享收藏
查看更多内容