Simple object detection framework without training
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| Autores: | , , |
|---|---|
| 格式: | comunicación de congreso |
| Fecha de Publicación: | 2025 |
| 實物特徵: | This research introduces a simple framework for Object Detection (OD) based on few-shot methods and Visual Foundation Models (VFM). The framework comprises of three core modules: (i) object proposal, (ii) embedding creation, and (iii) object classification. We evaluated six distinct VFMs to generate the object proposals. We compared the performances of four feature extractors to optimize the object representation, including convolutional neural networks and transformer-based models. Furthermore, we investigated four few-shot methods for classifying objects using minimal labeled data. Our framework provides a scalable and cost-effective solution, specifically applied to OD for pineapple localization in the drone imagery of large pineapple fields, where labeled data are scarce and expensive. |
| País: | Kérwá |
| 機構: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| 語言: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/102299 |
| 在線閱讀: | https://hdl.handle.net/10669/102299 https://doi.org/10.1109/BIP63158.2024.10885396 |
| Palabra clave: | Object Detection OD Visual Foundation Models VFM few-shot methods agrotechnology agricultural technology |