# 3rd Place Solution to "Google Landmark Retrieval 2020"

arXiv: https://arxiv.org/abs/2008.10480 · HTML: https://mlsanity.com/paper/2008.10480/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Boundary-aware Backward-Compatible Representation via Adversarial Learning in Image Retrieval](https://mlsanity.com/paper/2305.02610/) (2023)
- [Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval](https://mlsanity.com/paper/2204.13919/) (2022)
- [2nd Place Solution to Google Landmark Retrieval 2021](https://mlsanity.com/paper/2110.04294/) (2021)
- [Darwinian Model Upgrades: Model Evolving with Selective Compatibility](https://mlsanity.com/paper/2210.06954/) (2022)
- [Rethinking Robust Representation Learning Under Fine-grained Noisy Faces](https://mlsanity.com/paper/2208.04352/) (2022)

## 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.