Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning

Nobline Yoo, Olga Russakovsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The goal of 2D human pose estimation (HPE) is to localize anatomical landmarks, given an image of a person in a pose. SOTA techniques make use of thousands of labeled figures (finetuning transformers or training deep CNNs), acquired using labor-intensive crowdsourcing. On the other hand, self-supervised methods re-frame the HPE task as a reconstruction problem, enabling them to leverage the vast amount of unlabeled visual data, though at the present cost of accuracy. In this work, we explore ways to improve self-supervised HPE. We (1) analyze the relationship between reconstruction quality and pose estimation accuracy, (2) develop a model pipeline that outperforms the baseline which inspired our work, using less than one-third the amount of training data, and (3) offer a new metric suitable for self-supervised settings that measures the consistency of predicted body part length proportions. We show that a combination of well-engineered reconstruction losses and inductive priors can help coordinate pose learning alongside reconstruction in a self-supervised paradigm.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3263-3272
Number of pages10
ISBN (Electronic)9798350307443
DOIs
StatePublished - 2023
Event2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 - Paris, France
Duration: Oct 2 2023Oct 6 2023

Publication series

NameProceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023

Conference

Conference2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Country/TerritoryFrance
CityParis
Period10/2/2310/6/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Keywords

  • Computer vision
  • Human pose estimation
  • Inductive prior
  • Self supervised learning

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