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arXiv · 2506.00333

Test-time Vocabulary Adaptation for Language-driven Object Detection

Mingxuan Liu·Tyler L. Hayes·Massimiliano Mancini·Elisa Ricci·Riccardo Volpi·Gabriela Csurka
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Abstract

Open-vocabulary object detection models allow users to freely specify a class vocabulary in natural language at test time, guiding the detection of desired objects. However, vocabularies can be overly broad or even mis-specified, hampering the overall performance of the detector. In this work, we propose a plug-and-play Vocabulary Adapter (VocAda) to refine the user-defined vocabulary, automatically tailoring it to categories that are relevant for a given image. VocAda does not require any training, it operates at inference time in three steps: i) it uses an image captionner to describe visible objects, ii) it parses nouns from those captions, and iii) it selects relevant classes from the user-defined vocabulary, discarding irrelevant ones. Experiments on COCO and Objects365 with three state-of-the-art detectors show that VocAda consistently improves performance, proving its versatility. The code is open source.

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Mingxuan Liu, Tyler L. Hayes, Massimiliano Mancini, Elisa Ricci, Riccardo Volpi, Gabriela Csurka. 2025-05-31. Test-time Vocabulary Adaptation for Language-driven Object Detection. https://arxiv.org/abs/2506.00333

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