2nd Place Solution to Google Landmark Recognition Competition 2021

Abstract

As Transformer-based architectures have recently shown encouraging progresses in computer vision. In this work, we present the solution to the Google Landmark Recognition 2021 Challenge held on Kaggle, which is an improvement on our last year's solution by changing three designs, including (1) Using Swin and CSWin as backbone for feature extraction, (2) Train on full GLDv2, and (3) Using full GLDv2 images as index image set for kNN search. With these modifications, our solution significantly improves last year solution on this year competition. Our full pipeline, after ensembling Swin, CSWin, EfficientNet B7 models, scores 0.4907 on the private leaderboard which help us to get the 2nd place in the competition.

References in corpus (6)

• CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows
• Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse Dataset
• Attention-Aware Generalized Mean Pooling for Image Retrieval
• Team JL Solution to Google Landmark Recognition 2019
• 2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
• 1st Place Solution to Google Landmark Retrieval 2020