Exploring Open-Vocabulary Models for Category-Free Detection
Object detection models typically rely on a predefined setof categories, limiting their applicability in real-world scenarios whereobject classes may be unknown. In this paper, we propose a novel,training-free framework that enables off-the-shelf open-vocabulary ob-ject detectors (OvOD) to perform c...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/43664 |
| Acceso en línea: | https://hdl.handle.net/10347/43664 |
| Access Level: | acceso abierto |
| Palabra clave: | Category-free Open-vocabulary object detection Captioning |
| Sumario: | Object detection models typically rely on a predefined setof categories, limiting their applicability in real-world scenarios whereobject classes may be unknown. In this paper, we propose a novel,training-free framework that enables off-the-shelf open-vocabulary ob-ject detectors (OvOD) to perform category-free detection —localizingand classifying objects without any prior category knowledge. Our ap-proach leverages image captioning to dynamically generate descriptiveterms directly from the image content, followed by a WordNet-based fil-tering process to extract semantically meaningful category names. Thesediscovered categories are then embedded and matched with visual regionfeatures using a frozen OvOD model to perform detection. We evaluateour method on the COCO dataset in a fully zero-shot setting and demon-strate that it significantly outperforms strong multimodal large languagemodel baselines, achieving an improvement of over 30 AP points. Thishighlights our method as a promising direction for more adaptive solu-tions to real-world detection challenges. |
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