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arXiv · 2603.12409

ABRA: Teleporting Fine-Tuned Knowledge Across Domains for Open-Vocabulary Object Detection

Mattia Bernardi·Chiara Cappellino·Matteo Mosconi·Enver Sangineto·Angelo Porrello·Simone Calderara
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Abstract

Although recent Open-Vocabulary Object Detection architectures, such as Grounding DINO, demonstrate strong zero-shot capabilities, their performance degrades significantly under domain shifts. Moreover, many domains of practical interest, such as nighttime or foggy scenes, lack large annotated datasets, preventing direct fine-tuning. In this paper, we introduce Aligned Basis Relocation for Adaptation(ABRA), a method that transfers class-specific detection knowledge from a labeled source domain to a target domain where no training images containing these classes are accessible. ABRA formulates this adaptation as a geometric transport problem in the weight space of a pretrained detector, aligning source and target domain experts to transport class-specific knowledge. Extensive experiments across challenging domain shifts demonstrate that ABRA successfully teleports class-level specialization under multiple adverse conditions. Our code will be made public upon acceptance.

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Mattia Bernardi, Chiara Cappellino, Matteo Mosconi, Enver Sangineto, Angelo Porrello, Simone Calderara. 2026-03-12. ABRA: Teleporting Fine-Tuned Knowledge Across Domains for Open-Vocabulary Object Detection. https://arxiv.org/abs/2603.12409

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