# Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset

arXiv: https://arxiv.org/abs/1906.04087 · HTML: https://mlsanity.com/paper/1906.04087/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [Deep Learning for Instance Retrieval: A Survey](https://mlsanity.com/paper/2101.11282/) (2021)
- [Noise-resistant Deep Metric Learning with Ranking-based Instance Selection](https://mlsanity.com/paper/2103.16047/) (2021)
- [Contrastive Learning with Large Memory Bank and Negative Embedding Subtraction for Accurate Copy Detection](https://mlsanity.com/paper/2112.04323/) (2021)
- [InsCLR: Improving Instance Retrieval with Self-Supervision](https://mlsanity.com/paper/2112.01390/) (2021)
- [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)
- [EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing](https://mlsanity.com/paper/2208.14657/) (2022)
- [Darwinian Model Upgrades: Model Evolving with Selective Compatibility](https://mlsanity.com/paper/2210.06954/) (2022)
- [Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers](https://mlsanity.com/paper/2110.03786/) (2021)
- [Deep Learning Based Image Retrieval in the JPEG Compressed Domain](https://mlsanity.com/paper/2107.03648/) (2021)
- [2nd Place Solution to Google Landmark Recognition Competition 2021](https://mlsanity.com/paper/2110.02638/) (2021)
- [Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection](https://mlsanity.com/paper/2501.11063/) (2025)
- [Noise-Resistant Deep Metric Learning with Probabilistic Instance Filtering](https://mlsanity.com/paper/2108.01431/) (2021)
- [Google Landmark Retrieval 2021 Competition Third Place Solution](https://mlsanity.com/paper/2110.04619/) (2021)
- [NPR: Nocturnal Place Recognition in Streets](https://mlsanity.com/paper/2304.00276/) (2023)
- [NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer](https://mlsanity.com/paper/2402.17159/) (2024)
- [MARs: Multi-view Attention Regularizations for Patch-based Feature Recognition of Space Terrain](https://mlsanity.com/paper/2410.05182/) (2024)

## Abstract (arXiv)

The Google-Landmarks-v2 dataset is the biggest worldwide landmarks dataset characterized by a large magnitude of noisiness and diversity. We present a novel landmark retrieval/recognition system, robust to a noisy and diverse dataset, by our team, smlyaka. Our approach is based on deep convolutional neural networks with metric learning, trained by cosine-softmax based losses. Deep metric learning methods are usually sensitive to noise, and it could hinder to learn a reliable metric. To address this issue, we develop an automated data cleaning system. Besides, we devise a discriminative re-ranking method to address the diversity of the dataset for landmark retrieval. Using our methods, we achieved 1st place in the Google Landmark Retrieval 2019 challenge and 3rd place in the Google Landmark Recognition 2019 challenge on Kaggle.