3rd Place Solution to "Google Landmark Retrieval 2020"
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Abstract (arXiv)
Image retrieval is a fundamental problem in computer vision. This paper presents our 3rd place detailed solution to the Google Landmark Retrieval 2020 challenge. We focus on the exploration of data cleaning and models with metric learning. We use a data cleaning strategy based on embedding clustering. Besides, we employ a data augmentation method called Corner-Cutmix, which improves the model's ability to recognize multi-scale and occluded landmark images. We show in detail the ablation experiments and results of our method.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1.
5 papers.
- 2023Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrievalcited by 11
- 2022Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrievalcited by 9
- 20212nd Place Solution to Google Landmark Retrieval 2021cited by 5
- 2022Darwinian Model Upgrades: Model Evolving with Selective Compatibilitycited by 5
- 2022Rethinking Robust Representation Learning Under Fine-grained Noisy Facescited by 1
- 2023Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval4 May 2023
- 2022Darwinian Model Upgrades: Model Evolving with Selective Compatibility13 Oct 2022
- 2022Rethinking Robust Representation Learning Under Fine-grained Noisy Faces8 Aug 2022
- 2022Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval29 Apr 2022
- 20212nd Place Solution to Google Landmark Retrieval 20218 Oct 2021