{"ID":6023536,"CreatedAt":"2026-07-08T01:00:23.257252134Z","UpdatedAt":"2026-07-10T11:42:49.717029521Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06176","arxiv_id":"2607.06176","title":"Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.06176","url_pdf":"https://arxiv.org/pdf/2607.06176v1","authors":"[\"Runfeng Qu\",\"Pia K Bideau\",\"Ole Hall\",\"Julie Ouerfelli-Ethier\",\"Klaus Obermayer\",\"Olaf Hellwich\"]","published":"2026-07-07T11:53:32Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}