Scene Graph Generation (SGG) suffers from a long-tailed distribution, where a few predicate classes dominate while many others are underrepresented, leading to biased models that underperform on rare relations. Unbiased-SGG methods address this issue by implementing debiasing strategies, but often at the cost of spatial understanding, resulting in an over-reliance on semantic priors. We introduce Salience-SGG, a novel framework featuring an Iterative Salience Decoder (ISD) that emphasizes triplets with salient spatial structures. To support this, we propose semantic-agnostic salience labels guiding ISD. Evaluations on Visual Genome, Open Images V6, and GQA-200 show that Salience-SGG achieves state-of-the-art performance and improves existing Unbiased-SGG methods in their spatial understanding as demonstrated by the Pairwise Localization Average Precision
@article{arxiv.2601.08728,
title = {Salience-SGG: Enhancing Unbiased Scene Graph Generation with Iterative Salience Estimation},
author = {Runfeng Qu and Ole Hall and Pia K Bideau and Julie Ouerfelli-Ethier and Martin Rolfs and Klaus Obermayer and Olaf Hellwich},
journal= {arXiv preprint arXiv:2601.08728},
year = {2026}
}