Distributed training of CosPlace for large-scale visual place recognition

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.