Papers › Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Hyperbolic Vision Transformers: Combining Improvements in Metric Learning
Aleksandr Ermolov, Leyla Mirvakhabova, Valentin Khrulkov, Nicu Sebe, Ivan Oseledets
Metric learning aims to learn a highly discriminative model encouraging the embeddings of similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The common recipe is to use an encoder to extract embeddings and a distance-based loss function to match the representations -- usually, the Euclidean distance is utilized. An emerging interest in learning hyperbolic data embeddings suggests that hyperbolic geometry can be beneficial for natural data. Following this line of work, we propose a new hyperbolic-based model for metric learning. At the core of our method is a vision transformer with output embeddings mapped to hyperbolic space. These embeddings are directly optimized using modified pairwise cross-entropy loss. We evaluate the proposed model with six different formulations on four datasets achieving the new state-of-the-art performance. The source code is available at https://github.com/htdt/hyp_metric.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Metric Learning | CARS196 | Hyp-DINO 8x8 | R@1 | 92.8 | #2 of 36 | Archive leaderboard | report |
| Metric Learning | CARS196 | Hyp-DINO | R@1 | 89.2 | #13 of 36 | Archive leaderboard | report |
| Metric Learning | CARS196 | Hyp-ViT | R@1 | 86.5 | #25 of 36 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | Hyp-DINO | R@1 | 80.9 | #1 of 2 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | Hyp-ViT | R@1 | 85.6 | #3 of 30 | Archive leaderboard | report |
| Metric Learning | In-Shop | Hyp-ViT | R@1 | 92.5 | #4 of 15 | Archive leaderboard | report |
| Metric Learning | In-Shop | Hyp-DINO | R@1 | 92.4 | #5 of 15 | Archive leaderboard | report |
| Metric Learning | Stanford Online Products | Hyp-ViT | R@1 | 85.9 | #6 of 33 | Archive leaderboard | report |
| Metric Learning | Stanford Online Products | Hyp-DINO | R@1 | 85.1 | #7 of 33 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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