mlsanityData to 7 Oct 2026

Graph Edit Distance Reward: Learning to Edit Scene Graph

arXiv 2008.06651 ↗First posted 15 Aug 2020cs.CVcited by 14

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Abstract (arXiv)

Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA. In this paper, we propose a new method to edit the scene graph according to the user instructions, which has never been explored. To be specific, in order to learn editing scene graphs as the semantics given by texts, we propose a Graph Edit Distance Reward, which is based on the Policy Gradient and Graph Matching algorithm, to optimize neural symbolic model. In the context of text-editing image retrieval, we validate the effectiveness of our method in CSS and CRIR dataset. Besides, CRIR is a new synthetic dataset generated by us, which we will publish it soon for future use.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2020.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 2; 2024: 0; 2025: 3.

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