EFFECTIVE KNOWLEDGE DISTILLATION FOR HUMAN POSE ESTIMATION

被引：0

作者：

Zhou, Yang ^{[1
]}

Gu, Xiaofeng ^{[1
]}

Fu, Hong ^{[1
]}

Li, Na ^{[1
]}

Du, Xuemei ^{[1
]}

Kuang, Ping ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 611731, Peoples R China

来源：

2019 16TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICWAMTIP) | 2019年

关键词：

Human Pose Estimation; Knowledge Distillation;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Most existing human pose estimation approaches focus on improving the model performance, but putting aside the significant efficiency problem. It makes the model not practical. Therefore, it is meaningful to explore how to keep high precision on a smaller model. This paper investigates the knowledge distillation strategy for training small network by making use of large network and try to keep high performance at the same time. Experiments on COCO dataset demonstrates the effectiveness of the proposed approach which can improve network accuracy without increasing network complexity.

引用

页码：170 / 173

页数：4

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