A subgroup-based positive-pair selection framework lets noisy samples contribute to metric learning training instead of being discarded, improving retrieval and face recognition accuracy under label noise.
Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
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abstract
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.
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Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection
A subgroup-based positive-pair selection framework lets noisy samples contribute to metric learning training instead of being discarded, improving retrieval and face recognition accuracy under label noise.