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.

smlyaka 团队基于深度卷积神经网络与度量学习构建了一个鲁棒的人工智能系统，这个系统在 Google Landmarks-v2 数据集噪声和多样性较高的情况下表现出更好的识别和检索效果，其包括自动数据清理系统和提高数据区分度的再排序方法，并在 Google Landmark Retrieval 2019 挑战赛中获得了第一名，并在 Kaggle 的 Google Landmark Recognition 2019 挑战赛中位列第三。