# Few-shot target-driven instance detection based on open-vocabulary object detection models Source: [Few-shot target-driven instance detection based on open-vocabulary object detection models]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:31:06.502Z Authors: Ben Crulis, Barthelemy Serres, Cyril De Runz, Gilles Venturini Published: 2024-10-21T14:03:15.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()