Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, but their performance degrades under distribution shift. Test-time adaptation (TTA) offers a practical way to adapt models online using only unlabeled target data. However, despite substantial progress in TTA for vision-language classification, TTA for VLODs remains largely unexplored. The only prior method relies on a mean-teacher framework that introduces significant latency and memory overhead. To this end, we introduce \textsc{VLOD-TTA}, a TTA method that leverages dense proposal overlap and image-conditioned prompts to adapt VLODs with low additional overhead. \textsc{VLOD-TTA} combines (i) an IoU-weighted entropy objective that emphasizes spatially coherent proposal clusters and mitigates confirmation bias from isolated boxes, and (ii) image-conditioned prompt selection that ranks prompts by image-level compatibility and aggregates the most informative prompt scores for detection. Our experiments across diverse distribution shifts, including artistic domains, adverse driving conditions, low-light imagery, and common corruptions, indicate that \textsc{VLOD-TTA} consistently outperforms standard TTA baselines and the prior state-of-the-art method using YOLO-World and Grounding DINO. Code : https://github.com/imatif17/VLOD-TTA
@article{arxiv.2510.00458,
title = {VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors},
author = {Atif Belal and Heitor R. Medeiros and Marco Pedersoli and Eric Granger},
journal= {arXiv preprint arXiv:2510.00458},
year = {2026}
}