研究人员开发了一种名为Dual-SGG的新方法，通过结合基于检测器和基于查询的推理机制来改进场景图生成（SGG）。该方法解决了这两种现有SGG方法之间观察到的差异。通过分析基于检测器的可达性所产生的预测差异，该研究发现了为Dual-SGG设计提供信息的互补见解。在Visual Genome、Open Images v6和GQA-200数据集上进行的实验证明了这种整合方法的有效性。 AI
影响 这项研究通过提高场景图生成能力，可能带来更准确、更全面的视觉理解系统。
排序理由 该集群描述了提交给arXiv的一篇研究论文中提出的一种新方法，详细介绍了在特定数据集上的实验结果。
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
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 systematica…
arXiv:2607.06176v1 Announce Type: new 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 the…
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 systematica…