Scene Graph Generation (SGG) encodes visual relationships between objects in images as graph structures. Thanks to the advances of Vision-Language Models (VLMs), the task of Open-Vocabulary SGG has been recently proposed where models are evaluated on their functionality to learn a wide and diverse range of relations. Current benchmarks in SGG, however, possess a very limited vocabulary, making the evaluation of open-source models inefficient. In this paper, we propose a new reference-free metric to fairly evaluate the open-vocabulary capabilities of VLMs for relation prediction. Another limitation of Open-Vocabulary SGG is the reliance on weakly supervised data of poor quality for pre-training. We also propose a new solution for quickly generating high-quality synthetic data through region-specific prompt tuning of VLMs. Experimental results show that pre-training with this new data split can benefit the generalization capabilities of Open-Voc SGG models.
@article{arxiv.2509.01209,
title = {Measuring Image-Relation Alignment: Reference-Free Evaluation of VLMs and Synthetic Pre-training for Open-Vocabulary Scene Graph Generation},
author = {Maëlic Neau and Zoe Falomir and Cédric Buche and Akihiro Sugimoto},
journal= {arXiv preprint arXiv:2509.01209},
year = {2025}
}