Papers › Retrieval-Augmented Open-Vocabulary Object Detection
Retrieval-Augmented Open-Vocabulary Object Detection
Jooyeon Kim, Eulrang Cho, Sehyung Kim, Hyunwoo J. Kim
Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, iPod, and alligator. To extend the previous methods in two aspects, we propose Retrieval-Augmented Losses and visual Features (RALF). Our method retrieves related 'negative' classes and augments loss functions. Also, visual features are augmented with 'verbalized concepts' of classes, e.g., worn on the feet, handheld music player, and sharp teeth. Specifically, RALF consists of two modules: Retrieval Augmented Losses (RAL) and Retrieval-Augmented visual Features (RAF). RAL constitutes two losses reflecting the semantic similarity with negative vocabularies. In addition, RAF augments visual features with the verbalized concepts from a large language model (LLM). Our experiments demonstrate the effectiveness of RALF on COCO and LVIS benchmark datasets. We achieve improvement up to 3.4 box AP₅₀ᴺ on novel categories of the COCO dataset and 3.6 mask APᵣ gains on the LVIS dataset. Code is available at https://github.com/mlvlab/RALF .
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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 | RALF | AP novel-LVIS base training | 21.9 | #20 of 28 | Archive leaderboard | report |
| Open Vocabulary Object Detection | MSCOCO | RALF | AP 0.5 | 41.3 | #12 of 32 | Archive leaderboard | report |
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