Salience-SGG: Enhancing Unbiased Scene Graph Generation with Iterative Salience Estimation

Scene Graph Generation (SGG) suffers from a long-taileddistribution, where a few predicate classes dominate whilemany others are underrepresented, leading to biased mod-els that underperform on rare relations. Unbiased-SGGmethods address this by implementing debiasing strategies,but often at the cost of spatial understanding—resulting inover-reliance on semantic priors. We introduce Salience-SGG, a novel framework featuring an Iterative SalienceDecoder (ISD) that emphasizes triplets with salient spatialstructures. To support this, we propose semantic-agnosticsalience labels guiding ISD. Evaluations on Visual Genome,Open Images V6, and GQA-200 show that Salience-SGGachieves state-of-the-art performance and improves exist-ing Unbiased-SGG methods.