Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability
Authors: Runfeng Qu, Pia K Bideau, Ole Hall, Julie Ouerfelli-Ethier, Klaus Obermayer, Olaf Hellwich
Organizations: Technische Universität Berlin, Germany · Science of Intelligence Research Cluster of Excellence, Germany · Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, France · Humboldt Universität zu Berlin, Germany · Bernstein Center for Computational Neuroscience, Germany
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.