Weakly-Supervised Scene Graph Generation (WSSGG) research has recently emerged as an alternative to the fully-supervised approach that heavily relies on costly annotations. In this regard, studies on WSSGG have utilized image captions to obtain unlocalized triplets while primarily focusing on grounding the unlocalized triplets over image regions. However, they have overlooked the two issues involved in the triplet formation process from the captions: 1) Semantic over-simplification issue arises when extracting triplets from captions, where fine-grained predicates in captions are undesirably converted into coarse-grained predicates, resulting in a long-tailed predicate distribution, and 2) Low-density scene graph issue arises when aligning the triplets in the caption with entity/predicate classes of interest, where many triplets are discarded and not used in training, leading to insufficient supervision. To tackle the two issues, we propose a new approach, i.e., Large Language Model for weakly-supervised SGG (LLM4SGG), where we mitigate the two issues by leveraging the LLM's in-depth understanding of language and reasoning ability during the extraction of triplets from captions and alignment of entity/predicate classes with target data. To further engage the LLM in these processes, we adopt the idea of Chain-of-Thought and the in-context few-shot learning strategy. To validate the effectiveness of LLM4SGG, we conduct extensive experiments on Visual Genome and GQA datasets, showing significant improvements in both Recall@K and mean Recall@K compared to the state-of-the-art WSSGG methods. A further appeal is that LLM4SGG is data-efficient, enabling effective model training with a small amount of training images.

翻译：弱监督场景图生成（WSSGG）研究近期作为全监督方法（严重依赖昂贵标注）的替代方案而兴起。在此背景下，WSSGG研究利用图像标题获取非定位三元组，并主要聚焦于在图像区域上定位这些非定位三元组。然而，这些研究忽视了从标题生成三元组过程中涉及的两个问题：1）从标题提取三元组时出现的语义过度简化问题——标题中的细粒度谓词被不合理地转换为粗粒度谓词，导致谓词分布呈现长尾特性；2）将标题中的三元组与目标实体/谓词类别对齐时出现的低密度场景图问题——大量三元组被丢弃而未用于训练，导致监督信号不足。为解决这两个问题，我们提出新方法LLM4SGG（面向弱监督SGG的大语言模型），该方法通过利用大语言模型（LLM）对语言的深度理解与推理能力，在从标题提取三元组及将实体/谓词类别与目标数据对齐的过程中缓解上述问题。为进一步引导LLM参与这些过程，我们采纳了思维链（Chain-of-Thought）思想及上下文少样本学习策略。为验证LLM4SGG的有效性，我们在Visual Genome和GQA数据集上开展广泛实验，结果表明Recall@K与平均Recall@K指标均显著优于现有最优WSSGG方法。值得关注的是，LLM4SGG具有数据高效特性，仅需少量训练图像即可实现有效模型训练。