# Test-time Vocabulary Adaptation for Language-driven Object Detection

Source: [Test-time Vocabulary Adaptation for Language-driven Object Detection](<https://www.sota2.com/research/paper/68fa78fb1e209131cea7b785>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:50:02.046Z

Authors: Mingxuan Liu, Tyler L. Hayes, Massimiliano Mancini, Elisa Ricci, Riccardo Volpi, Gabriela Csurka

Published: 2025-05-31T01:15:29.000Z

## 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.

## 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](<https://arxiv.org/abs/2506.00333>)

[PDF](<https://arxiv.org/pdf/2506.00333.pdf>)