3rd Place Solution for Google Universal Image Embedding
Not on the map. Why?
Abstract (arXiv)
This paper presents the 3rd place solution to the Google Universal Image Embedding Competition on Kaggle. We use ViT-H/14 from OpenCLIP for the backbone of ArcFace, and trained in 2 stage. 1st stage is done with freezed backbone, and 2nd stage is whole model training. We achieve 0.692 mean Precision @5 on private leaderboard. Code available at https://github.com/YasumasaNamba/google-universal-image-embedding
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 14 Oct 2022.
Builds on
0 of 0 references.
No references here yet.
Built on by
Citing papers per year: 2023: 1.
1 paper.