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.
翻译:在本文中,我们展示了2021年Google Landmark承认竞争的解决方案。 首先,嵌入的图像是通过各种建筑(即CNN、变形器和混合型)提取的,这些建筑因ArcFace损失而优化。 然后,我们运用高效管道,通过调整分类登录和非地标转移器的检索分数来重新排序预测。 最后,集成模型在私人领头板上赢得了0.489分,在2021年Google Landmark识别竞赛中达到了第3位。