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English(EN) Reasoning-Aware Relational Representation Learning for Open-Vocabulary Scene Graph Generation

新框架RaRe利用LLM的推理过程增强开放词汇场景图生成

研究人员开发了RaRe,一个用于开放词汇场景图生成(OV-SGG)的新颖框架。该方法利用多模态大语言模型(MLLMs)生成思维链(CoT)推理过程,然后将其转化为结构化的关系嵌入。与仅将CoT作为中间步骤的先前方法不同,RaRe直接优化这些由推理过程派生的表示,以增强不同关系之间的区分度。该框架通过自监督对比学习将推理过程嵌入与文本关系描述对齐,并使用促进语义特定关系证据的强化学习目标来优化推理过程的生成。 AI

影响 这项研究通过增强对物体之间关系的识别能力,特别是稀有关系,有望提高AI理解和描述复杂视觉场景的能力。

排序理由 该项目是一篇研究论文,详细介绍了一种新的场景图生成方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新框架RaRe利用LLM的推理过程增强开放词汇场景图生成

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该项目是一篇研究论文,详细介绍了一种新的场景图生成方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiawei Xiao, Boya Wang, Longtian Qiu, Shan Ning, Xuming He ·

    面向开放词汇场景图生成的推理感知关系表示学习

    arXiv:2611.13496v1 Announce Type: cross Abstract: Open-Vocabulary Scene Graph Generation (OV-SGG) requires models to recognize visual relationships beyond the training vocabulary, yet existing methods often rely on dataset-specific object--predicate co-occurrence patterns. Under …