Papers › LP-OVOD: Open-Vocabulary Object Detection by Linear Probing
LP-OVOD: Open-Vocabulary Object Detection by Linear Probing
Chau Pham, Truong Vu, Khoi Nguyen
This paper addresses the challenging problem of open-vocabulary object detection (OVOD) where an object detector must identify both seen and unseen classes in test images without labeled examples of the unseen classes in training. A typical approach for OVOD is to use joint text-image embeddings of CLIP to assign box proposals to their closest text label. However, this method has a critical issue: many low-quality boxes, such as over- and under-covered-object boxes, have the same similarity score as high-quality boxes since CLIP is not trained on exact object location information. To address this issue, we propose a novel method, LP-OVOD, that discards low-quality boxes by training a sigmoid linear classifier on pseudo labels retrieved from the top relevant region proposals to the novel text. Experimental results on COCO affirm the superior performance of our approach over the state of the art, achieving 40.5 in APₙₒᵥₑₗ using ResNet50 as the backbone and without external datasets or knowing novel classes during training. Our code will be available at https://github.com/VinAIResearch/LP-OVOD.
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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 | MSCOCO | LP-OVOD (OWL-ViT Proposals) | AP 0.5 | 44.9 | #6 of 32 | Archive leaderboard | report |
| Open Vocabulary Object Detection | MSCOCO | LP-OVOD | AP 0.5 | 40.5 | #13 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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