2nd Place Solution to Google Landmark Recognition Competition 2021
원문 초록
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.
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A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal Endoscopy
Efficient large-scale image retrieval with deep feature orthogonality and Hybrid-Swin-Transformers
Google Landmark Recognition 2020 Competition Third Place Solution
Supporting large-scale image recognition with out-of-domain samples
Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
토론
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