# Graph Edit Distance Reward: Learning to Edit Scene Graph

arXiv: https://arxiv.org/abs/2008.06651 · HTML: https://mlsanity.com/paper/2008.06651/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [Graph Matching with Bi-level Noisy Correspondence](https://mlsanity.com/paper/2212.04085/) (2022)
- [Cycle-Consistent Inverse GAN for Text-to-Image Synthesis](https://mlsanity.com/paper/2108.01361/) (2021)
- [A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs](https://mlsanity.com/paper/2106.04927/) (2021)
- [CLEVR_HYP: A Challenge Dataset and Baselines for Visual Question Answering with Hypothetical Actions over Images](https://mlsanity.com/paper/2104.05981/) (2021)
- [Reasoning about Actions over Visual and Linguistic Modalities: A Survey](https://mlsanity.com/paper/2207.07568/) (2022)
- [NEUCORE: Neural Concept Reasoning for Composed Image Retrieval](https://mlsanity.com/paper/2310.01358/) (2023)
- [Statistical Confidence Rescoring for Robust 3D Scene Graph Generation from Multi-View Images](https://mlsanity.com/paper/2508.06546/) (2025)
- [Towards Unsupervised Training of Matching-based Graph Edit Distance Solver via Preference-aware GAN](https://mlsanity.com/paper/2506.01977/) (2025)
- [Scene Graph Modification as Incremental Structure Expanding](https://mlsanity.com/paper/2209.09093/) (2022)
- [Learning Action-Effect Dynamics for Hypothetical Vision-Language Reasoning Task](https://mlsanity.com/paper/2212.03866/) (2022)
- [Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach](https://mlsanity.com/paper/2305.14410/) (2023)
- [DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching](https://mlsanity.com/paper/2503.18245/) (2025)
- [Learning Action-Effect Dynamics from Pairs of Scene-graphs](https://mlsanity.com/paper/2212.03433/) (2022)

## 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.