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Distributed training of CosPlace for large-scale visual place recognition.

Riccardo Zaccone1, Gabriele Berton1, Carlo Masone1

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Summary

Visual place recognition (VPR) models like CosPlace can be improved. We propose a new formulation to overcome suboptimal sequential training, enabling faster large-scale image retrieval for VPR.

Keywords:
deep learningdistributed learningimage retrievalvisual geolocalizationvisual place recognition

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Visual place recognition (VPR) identifies geographic locations using images.
  • Current VPR methods often use contrastive learning for image retrieval, limiting large-scale dataset training.
  • CosPlace introduced a classification-based approach for VPR, enabling better learning from large datasets.

Purpose of the Study:

  • To analyze CosPlace's performance from a continual learning perspective.
  • To identify and address suboptimal results from CosPlace's sequential training.
  • To propose an improved formulation for VPR training.

Main Methods:

  • Experimental analysis of CosPlace under continual learning conditions.
  • Development of a novel formulation to address training limitations.
  • Evaluation of the proposed method for efficiency and effectiveness in large-scale image retrieval.

Main Results:

  • CosPlace's sequential training procedure yields suboptimal performance in VPR.
  • The proposed formulation effectively resolves the identified training pitfalls.
  • The new approach facilitates faster and more efficient distributed training for VPR models.

Conclusions:

  • Sequential training strategies can hinder VPR model performance.
  • A revised formulation offers a more effective and efficient training paradigm for VPR.
  • Further research is needed to optimize large-scale image retrieval for VPR.