Pay Attention Selectively and Comprehensively: Pyramid Gating Network for Human Pose Estimation without Pre-training

Chenru Jiang, Kaizhu Huang*, Shufei Zhang, Xinheng Wang, Jimin Xiao

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

8 Citations (Scopus)

Abstract

Deep neural network with multi-scale feature fusion has achieved great success in human pose estimation. However, drawbacks still exist in these methods: 1) they consider multi-scale features equally, which may over-emphasize redundant features; 2) preferring deeper structures, they can learn features with the strong semantic representation, but tend to lose natural discriminative information; 3) to attain good performance, they rely heavily on pretraining, which is time-consuming, or even unavailable practically. To mitigate these problems, we propose a novel comprehensive recalibration model called Pyramid GAting Network (PGA-Net) that is capable of distillating, selecting, and fusing the discriminative and attention-aware features at different scales and different levels (i.e., both semantic and natural levels). Meanwhile, focusing on fusing features both selectively and comprehensively, PGA-Net can demonstrate remarkable stability and encouraging performance even without pre-training, making the model can be trained truly from scratch. We demonstrate the effectiveness of PGA-Net through validating on COCO and MPII benchmarks, attaining new state-of-the-art performance. https://github.com/ssr0512/PGA-Net

Original languageEnglish
Title of host publicationMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages2364-2371
Number of pages8
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - 12 Oct 2020
Event28th ACM International Conference on Multimedia, MM 2020 - Virtual, Online, United States
Duration: 12 Oct 202016 Oct 2020

Publication series

NameMM 2020 - Proceedings of the 28th ACM International Conference on Multimedia

Conference

Conference28th ACM International Conference on Multimedia, MM 2020
Country/TerritoryUnited States
CityVirtual, Online
Period12/10/2016/10/20

Keywords

  • human pose estimation
  • pyramid gating system
  • stabilization

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