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Learning from image-text data has demonstrated recent success for manyrecognition tasks, yet is currently limited to visual features or individualvisual concepts such as objects. In this paper, we propose one of the firstmethods that learn from image-sentence pairs to extract a graphicalrepresentation of localized objects and their relationships within an image,known as scene graph. To bridge the gap between images and texts, we leveragean off-the-shelf object detector to identify and localize object instances,match labels of detected regions to concepts parsed from captions, and thuscreate "pseudo" labels for learning scene graph. Further, we design aTransformer-based model to predict these "pseudo" labels via a masked tokenprediction task. Learning from only image-sentence pairs, our model achieves30% relative gain over a latest method trained with human-annotated unlocalizedscene graphs. Our model also shows strong results for weakly and fullysupervised scene graph generation. In addition, we explore an open-vocabularysetting for detecting scene graphs, and present the first result for open-setscene graph generation. Our code is available athttps://github.com/YiwuZhong/SGG_from_NLS.

Learning to Generate Scene Graph from Natural Language Supervision