Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
This work addresses landmark retrieval and recognition for applications like image search and tourism, but it is incremental as it builds on existing deep learning and metric learning techniques.
The authors tackled landmark retrieval and recognition using the noisy and diverse Google-Landmarks-v2 dataset by developing a robust system based on deep convolutional neural networks with metric learning, achieving 1st place in retrieval and 3rd place in recognition in the 2019 Kaggle challenges.
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.