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Visual Person Understanding Through Multi-task and Multi-dataset Learning

  • Kilian Pfeiffer
  • Alexander HermansEmail author
  • István Sárándi
  • Mark Weber
  • Bastian Leibe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11824)

Abstract

We address the problem of learning a single model for person re-identification, attribute classification, body part segmentation, and pose estimation. With predictions for these tasks we gain a more holistic understanding of persons, which is valuable for many applications. This is a classical multi-task learning problem. However, no dataset exists that these tasks could be jointly learned from. Hence several datasets need to be combined during training, which in other contexts has often led to reduced performance in the past. We extensively evaluate how the different task and datasets influence each other and how different degrees of parameter sharing between the tasks affect performance. Our final model matches or outperforms its single-task counterparts without creating significant computational overhead, rendering it highly interesting for resource-constrained scenarios such as mobile robotics.

Notes

Acknowledgements

This project was funded, in parts, by ERC Consolidator Grant project “DeeViSe” (ERC-CoG-2017-773161) and the BMBF projects “FRAME” (16SV7830) and “PARIS” (16ES0602). Istvan Sarandi’s research is funded by a grant from the Bosch Research Foundation. Most experiments were performed on the RWTH Aachen University CLAIX 2018 GPU Cluster.

Supplementary material

480714_1_En_39_MOESM1_ESM.zip (44.6 mb)
Supplementary material 1 (zip 45660 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kilian Pfeiffer
    • 1
  • Alexander Hermans
    • 1
    Email author
  • István Sárándi
    • 1
  • Mark Weber
    • 1
  • Bastian Leibe
    • 1
  1. 1.Visual Computing InstituteRWTH Aachen UniversityAachenGermany

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