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Learning scene graphs from natural language descriptions has proven to be acheap and promising scheme for Scene Graph Generation (SGG). However, suchunstructured caption data and its processing are troubling the learning anacurrate and complete scene graph. This dilema can be summarized as threepoints. First, traditional language parsers often fail to extract meaningfulrelationship triplets from caption data. Second, grounding unlocalized objectsin parsed triplets will meet ambiguity in visual-language alignment. Last,caption data typically are sparse and exhibit bias to partial observations ofimage content. These three issues make it hard for the model to generatecomprehensive and accurate scene graphs. To fill this gap, we propose a simpleyet effective framework, GPT4SGG, to synthesize scene graphs from holistic andregion-specific narratives. The framework discards traditional language parser,and localize objects before obtaining relationship triplets. To obtainrelationship triplets, holistic and dense region-specific narratives aregenerated from the image. With such textual representation of image data and atask-specific prompt, an LLM, particularly GPT-4, directly synthesizes a scenegraph as "pseudo labels". Experimental results showcase GPT4SGG significantlyimproves the performance of SGG models trained on image-caption data. Webelieve this pioneering work can motivate further research into mining thevisual reasoning capabilities of LLMs.

GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives