3rd Place Solution to Google Landmark Recognition Competition 2021

Abstract

In this paper, we show our solution to the Google Landmark Recognition 2021 Competition. Firstly, embeddings of images are extracted via various architectures (i.e. CNN-, Transformer- and hybrid-based), which are optimized by ArcFace loss. Then we apply an efficient pipeline to re-rank predictions by adjusting the retrieval score with classification logits and non-landmark distractors. Finally, the ensembled model scores 0.489 on the private leaderboard, achieving the 3rd place in the 2021 edition of the Google Landmark Recognition Competition.

arXiv commentCorrected typos

References in corpus (7)

• Improved Regularization of Convolutional Neural Networks with Cutout
• EfficientNetV2: Smaller Models and Faster Training
• CvT: Introducing Convolutions to Vision Transformers
• Attention-Aware Generalized Mean Pooling for Image Retrieval
• Google Landmark Recognition 2020 Competition Third Place Solution
• Supporting large-scale image recognition with out-of-domain samples
• Towards A Fairer Landmark Recognition Dataset