Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation

Mehrdad Noori; David Osowiechi; Gustavo Adolfo Vargas Hakim; Ali Bahri; Moslem Yazdanpanah; Sahar Dastani; Farzad Beizaee; Ismail Ben Ayed; Christian Desrosiers

arXiv · 2505.21844

Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation

Mehrdad Noori·David Osowiechi·Gustavo Adolfo Vargas Hakim·Ali Bahri·Moslem Yazdanpanah·Sahar Dastani·Farzad Beizaee·Ismail Ben Ayed·Christian Desrosiers

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Abstract

Recently, test-time adaptation has attracted wide interest in the context of vision-language models for image classification. However, to the best of our knowledge, the problem is completely overlooked in dense prediction tasks such as Open-Vocabulary Semantic Segmentation (OVSS). In response, we propose a novel TTA method tailored to adapting VLMs for segmentation during test time. Unlike TTA methods for image classification, our Multi-Level and Multi-Prompt (MLMP) entropy minimization integrates features from intermediate vision-encoder layers and is performed with different text-prompt templates at both the global CLS token and local pixel-wise levels. Our approach could be used as plug-and-play for any segmentation network, does not require additional training data or labels, and remains effective even with a single test sample. Furthermore, we introduce a comprehensive OVSS TTA benchmark suite, which integrates a rigorous evaluation protocol, nine segmentation datasets, 15 common synthetic corruptions, and additional real and rendered domain shifts, \textbf{with a total of 87 distinct test scenarios}, establishing a standardized and comprehensive testbed for future TTA research in open-vocabulary segmentation. Our experiments on this suite demonstrate that our segmentation-tailored method consistently delivers significant gains over direct adoption of TTA classification baselines. Code and data are available at https://github.com/dosowiechi/MLMP.

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Mehrdad Noori, David Osowiechi, Gustavo Adolfo Vargas Hakim, Ali Bahri, Moslem Yazdanpanah, Sahar Dastani, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers. 2025-11-09. Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation. https://arxiv.org/abs/2505.21844

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