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

• URL: http://arxiv.org/abs/2410.16028v1
• Date: Mon, 21 Oct 2024 14:03:15 GMT
• Title: Few-shot target-driven instance detection based on open-vocabulary object detection models
• Authors: Ben Crulis, Barthelemy Serres, Cyril De Runz, Gilles Venturini,
• Abstract summary: Open-vocabulary object detection models bring closer visual and textual concepts in the same latent space. We propose a lightweight method to turn the latter into a one-shot or few-shot object recognition models without requiring textual descriptions.
• Score: 1.0749601922718608
• License:
• 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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