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）研究近期作为依赖昂贵标注的全监督方法的替代方案而兴起。现有WSSGG研究利用图像标题获取非定位三元组，并主要聚焦于将非定位三元组与图像区域进行对齐。然而，这些研究忽略了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，即标题中的细粒度谓词被不期望地转化为粗粒度谓词，导致长尾谓词分布；2）将标题中三元组与目标实体/谓词类别对齐时引发的低密度场景图问题，大量三元组被丢弃而未用于训练，导致监督信息不足。针对这两个问题，我们提出新方法——弱监督SGG大语言模型（LLM4SGG），通过利用大语言模型在语言深度理解与推理能力方面的优势，在从标题提取三元组及实体/谓词类别与目标数据对齐过程中缓解上述问题。为进一步发挥大语言模型在这些过程中的作用，我们引入思维链（Chain-of-Thought）与上下文少样本学习策略。在Visual Genome和GQA数据集上的大量实验验证了LLM4SGG的有效性，其在Recall@K和平均Recall@K指标上均显著优于现有最优WSSGG方法。此外，LLM4SGG具有数据高效性，可通过少量训练图像实现有效模型训练。