1st Place Solution to Google Landmark Retrieval 2020
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Aug 2020.
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Citing papers per year: 2021: 3; 2022: 3.
6 papers.
- 2022Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrievalcited by 9
- 2022Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrievalcited by 6
- 20212nd Place Solution to Google Landmark Retrieval 2021cited by 5
- 2022Darwinian Model Upgrades: Model Evolving with Selective Compatibilitycited by 4
- 20212nd Place Solution to Google Landmark Recognition Competition 2021cited by 1
- 2021Google Landmark Retrieval 2021 Competition Third Place Solutioncited by 0
- 2022Darwinian Model Upgrades: Model Evolving with Selective Compatibility13 Oct 2022
- 2022Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval29 Apr 2022
- 2022Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval24 Jan 2022
- 2021Google Landmark Retrieval 2021 Competition Third Place Solution9 Oct 2021
- 20212nd Place Solution to Google Landmark Retrieval 20218 Oct 2021
- 20212nd Place Solution to Google Landmark Recognition Competition 20216 Oct 2021