Papers › Integrating Language Guidance into Vision-based Deep Metric Learning
Integrating Language Guidance into Vision-based Deep Metric Learning
Karsten Roth, Oriol Vinyals, Zeynep Akata
Deep Metric Learning (DML) proposes to learn metric spaces which encode semantic similarities as embedding space distances. These spaces should be transferable to classes beyond those seen during training. Commonly, DML methods task networks to solve contrastive ranking tasks defined over binary class assignments. However, such approaches ignore higher-level semantic relations between the actual classes. This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes, impacting the generalizability of the learned metric space. To tackle this issue, we propose a language guidance objective for visual similarity learning. Leveraging language embeddings of expert- and pseudo-classnames, we contextualize and realign visual representation spaces corresponding to meaningful language semantics for better semantic consistency. Extensive experiments and ablations provide a strong motivation for our proposed approach and show language guidance offering significant, model-agnostic improvements for DML, achieving competitive and state-of-the-art results on all benchmarks. Code available at https://github.com/ExplainableML/LanguageGuidance_for_DML.
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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 | ResNet50 + Language | R@1 | 90.2 | #8 of 36 | Archive leaderboard | report |
| Metric Learning | CUB-200-2011 | ResNet50 + Language | R@1 | 71.4 | #9 of 30 | Archive leaderboard | report |
| Metric Learning | Stanford Online Products | ResNet50 + Language | R@1 | 81.3 | #16 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.
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