Papers › RegionCLIP: Region-based Language-Image Pretraining
RegionCLIP: Region-based Language-Image Pretraining
Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, Jianfeng Gao
Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans. To mitigate this issue, we propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts. Our method leverages a CLIP model to match image regions with template captions and then pretrains our model to align these region-text pairs in the feature space. When transferring our pretrained model to the open-vocabulary object detection tasks, our method significantly outperforms the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respectively. Moreoever, the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets. Our code is available at https://github.com/microsoft/RegionCLIP.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Open Vocabulary Object Detection | LVIS v1.0 | Region-CLIP (RN50x4-C4) | AP novel-LVIS base training | 22.0 | #19 of 28 | Archive leaderboard | report |
| Open Vocabulary Object Detection | LVIS v1.0 | Region-CLIP (RN50-C4) | AP novel-LVIS base training | 17.1 | #26 of 28 | Archive leaderboard | report |
| Open Vocabulary Object Detection | MSCOCO | Region-CLIP (RN50x4-C4) | AP 0.5 | 39.3 | #14 of 32 | Archive leaderboard | report |
| Open Vocabulary Object Detection | MSCOCO | Region-CLIP (RN50-C4) | AP 0.5 | 31.4 | #23 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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