Unbiased Scene Graph Generation via Visual Language Models

Scene graph generation becomes significantly important as it bridges the gap between linguistic and visual information of scenes, facilitating a high-dimensional understanding of scenes. In this paper, we analyze the limitations of current scene graph generation methods induced by the inherent semantic relationship biases embedded in existing datasets. Furthermore, we propose a method to enhance scene graph by leveraging visual language models (VLMs). This approach leverages the strengths of VLMs in understanding and generating triplets with semantic predicates, ensuring unbiased and fine-grained scene graphs.