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arXiv · 2410.16028

Few-shot target-driven instance detection based on open-vocabulary object detection models

Ben Crulis·Barthelemy Serres·Cyril De Runz·Gilles Venturini
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Abstract

Current large open vision models could be useful for one and few-shot object recognition. Nevertheless, gradient-based re-training solutions are costly. On the other hand, open-vocabulary object detection models bring closer visual and textual concepts in the same latent space, allowing zero-shot detection via prompting at small computational cost. We propose a lightweight method to turn the latter into a one-shot or few-shot object recognition models without requiring textual descriptions. Our experiments on the TEgO dataset using the YOLO-World model as a base show that performance increases with the model size, the number of examples and the use of image augmentation.

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Ben Crulis, Barthelemy Serres, Cyril De Runz, Gilles Venturini. 2024-10-21. Few-shot target-driven instance detection based on open-vocabulary object detection models. https://arxiv.org/abs/2410.16028

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