Papers › CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching
CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching
Xiaoshi Wu, Feng Zhu, Rui Zhao, Hongsheng Li
Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks; (2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA^+ on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA^+ achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Described Object Detection | Description Detection Dataset | CORA-R50 | Intra-scenario ABS mAP | 5.0 | #8 of 8 | Archive leaderboard | report |
| Described Object Detection | Description Detection Dataset | CORA-R50 | Intra-scenario FULL mAP | 6.2 | #8 of 8 | Archive leaderboard | report |
| Described Object Detection | Description Detection Dataset | CORA-R50 | Intra-scenario PRES mAP | 6.7 | #8 of 8 | Archive leaderboard | report |
| Open Vocabulary Object Detection | MSCOCO | CORA+ | AP 0.5 | 43.1 | #8 of 32 | Archive leaderboard | report |
| Open Vocabulary Object Detection | MSCOCO | CORA | AP 0.5 | 41.7 | #11 of 32 | 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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