1st Place Solution to Google Landmark Retrieval 2020

2020/08/24 by SeungKee Jeon, Jeon, SeungKee
Computer Science · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Face recognition and analysis

paper · pdf · doi:10.48550/arxiv.2009.05132

openalex publication_date 2020/08/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents the 1st place solution to the Google Landmark Retrieval 2020 Competition on Kaggle. The solution is based on metric learning to classify numerous landmark classes, and uses transfer learning with two train datasets, fine-tuning on bigger images, adjusting loss weight for cleaner samples, and esemble to enhance the model's performance further. Finally, it scored 0.38677 mAP@100 on the private leaderboard.

Citations

• CosFace: Large Margin Cosine Loss for Deep Face Recognition
• Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
• Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval

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• 2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
• 3rd Place Solution to Google Landmark Recognition Competition 2021