Team JL Solution to Google Landmark Recognition 2019
This is an incremental solution to a specific computer vision competition problem for landmark recognition researchers and practitioners.
The paper tackled the Google Landmark Recognition 2019 Challenge by developing a retrieval-based method using global and local CNN approaches, achieving first place with a score of 0.37606 GAP on the private leaderboard.
In this paper, we describe our solution to the Google Landmark Recognition 2019 Challenge held on Kaggle. Due to the large number of classes, noisy data, imbalanced class sizes, and the presence of a significant amount of distractors in the test set, our method is based mainly on retrieval techniques with both global and local CNN approaches. Our full pipeline, after ensembling the models and applying several steps of re-ranking strategies, scores 0.37606 GAP on the private leaderboard which won the 1st place in the competition.