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arXiv · 2607.06176

Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability

Runfeng Qu·Pia K Bideau·Ole Hall·Julie Ouerfelli-Ethier·Klaus Obermayer·Olaf Hellwich
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Abstract

Scene graph generation (SGG) approaches can be broadly classified into detector-based and query-based methods according to their underlying reasoning mechanisms. However, the discrepancy in their predictive behaviors, induced by these distinct mechanisms, has not been systematically analyzed. In this work, we design a controlled experimental setup to examine prediction discrepancies from the perspective of detector-conditioned reachability. The results suggest clear complementary clues. Motivated by this observation, we introduce a Dual-SGG method that consolidates both reasoning mechanisms via a dual-query design, thereby leveraging the complementary predictive behaviors of both detector-based and query-based methods. Extensive experiments on the Visual Genome, Open Images v6, and GQA-200 datasets demonstrate the effectiveness of the proposed method.

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Runfeng Qu, Pia K Bideau, Ole Hall, Julie Ouerfelli-Ethier, Klaus Obermayer, Olaf Hellwich. 2026-07-07. Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability. https://arxiv.org/abs/2607.06176

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