Methods › Computer Vision › Image Data Augmentation › OA-Mix
Object-Aware Mix
OA-Mix
Introduced by Wooju Lee et al. in Object-Aware Domain Generalization for Object Detection
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
OA-Mix is a general and effective data augmentation method for single-domain generalization in object detection. It increases image diversity while preserving important semantic features with multi-level transformations and object-aware mixing.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Object-Aware Domain Generalization for Object Detection 19 Dec 2023 · 1 repository · arXiv:2312.12133
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Autonomous Driving | 1 |
| Contrastive Learning | 1 |
| Data Augmentation | 1 |
| Domain Generalization | 1 |
| Object | 1 |
| Object Detection | 1 |
| Object Localization | 1 |
| Robust Object Detection | 1 |
| object-detection | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections