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

Scene graph generation (SGG) approaches can be broadlyclassified into detector-based and query-based methods according to theirunderlying reasoning mechanisms. However, the discrepancy in their pre-dictive behaviors, induced by these distinct mechanisms, has not beensystematically analyzed. In this work, we design a controlled experimen-tal setup to examine prediction discrepancies from the perspective ofdetector-conditioned reachability. The results suggest clear complemen-tary clues. Motivated by this observation, we introduce a Dual-SGGmethod that consolidates both reasoning mechanisms via a dual-querydesign, thereby leveraging the complementary predictive behaviors ofboth detector-based and query-based methods. Extensive experimentson the Visual Genome, Open Images v6, and GQA-200 datasets demon-strate the effectiveness of the proposed method. Code is available at:Dual-SGG.