# Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning

arXiv: https://arxiv.org/abs/2208.08165 · HTML: https://mlsanity.com/paper/2208.08165/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 62 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 30 of 53)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [From Recognition to Cognition: Visual Commonsense Reasoning](https://mlsanity.com/paper/1811.10830/) (2018)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [Open-Vocabulary Object Detection Using Captions](https://mlsanity.com/paper/2011.10678/) (2020)
- [Neural Motifs: Scene Graph Parsing with Global Context](https://mlsanity.com/paper/1711.06640/) (2017)
- [Learning How to Ask: Querying LMs with Mixtures of Soft Prompts](https://mlsanity.com/paper/2104.06599/) (2021)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers](https://mlsanity.com/paper/2004.00849/) (2020)
- [3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera](https://mlsanity.com/paper/1910.02527/) (2019)
- [TransVG: End-to-End Visual Grounding with Transformers](https://mlsanity.com/paper/2104.08541/) (2021)
- [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018)
- [Auto-Encoding Scene Graphs for Image Captioning](https://mlsanity.com/paper/1812.02378/) (2018)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [Exploring Visual Relationship for Image Captioning](https://mlsanity.com/paper/1809.07041/) (2018)

## Built on by (most cited first, 30 of 54)

- [Read-only Prompt Optimization for Vision-Language Few-shot Learning](https://mlsanity.com/paper/2308.14960/) (2023)
- [From Pixels to Graphs: Open-Vocabulary Scene Graph Generation with Vision-Language Models](https://mlsanity.com/paper/2404.00906/) (2024)
- [Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World](https://mlsanity.com/paper/2303.13233/) (2023)
- [LLM4SGG: Large Language Models for Weakly Supervised Scene Graph Generation](https://mlsanity.com/paper/2310.10404/) (2023)
- [Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention](https://mlsanity.com/paper/2311.10988/) (2023)
- [Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models](https://mlsanity.com/paper/2305.12476/) (2023)
- [Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection](https://mlsanity.com/paper/2302.00268/) (2023)
- [HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation](https://mlsanity.com/paper/2411.18042/) (2024)
- [Probabilistic Prompt Learning for Dense Prediction](https://mlsanity.com/paper/2304.00779/) (2023)
- [Knowledge-Aligned Counterfactual-Enhancement Diffusion Perception for Unsupervised Cross-Domain Visual Emotion Recognition](https://mlsanity.com/paper/2505.19694/) (2025)
- [OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models](https://mlsanity.com/paper/2407.11213/) (2024)
- [Scene Graph Generation with Role-Playing Large Language Models](https://mlsanity.com/paper/2410.15364/) (2024)
- [VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2311.16492/) (2023)
- [Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking](https://mlsanity.com/paper/2505.12667/) (2025)
- [Open-Vocabulary Object Detection via Scene Graph Discovery](https://mlsanity.com/paper/2307.03339/) (2023)
- [Enhancing Scene Graph Generation with Hierarchical Relationships and Commonsense Knowledge](https://mlsanity.com/paper/2311.12889/) (2023)
- [Towards Lifelong Scene Graph Generation with Knowledge-ware In-context Prompt Learning](https://mlsanity.com/paper/2401.14626/) (2024)
- [Scene-Graph ViT: End-to-End Open-Vocabulary Visual Relationship Detection](https://mlsanity.com/paper/2403.14270/) (2024)
- [Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms](https://mlsanity.com/paper/2404.09231/) (2024)
- [Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2301.00351/) (2023)
- [Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting](https://mlsanity.com/paper/2310.18804/) (2023)
- [PRISM-0: A Predicate-Rich Scene Graph Generation Framework for Zero-Shot Open-Vocabulary Tasks](https://mlsanity.com/paper/2504.00844/) (2025)
- [GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking](https://mlsanity.com/paper/2602.17555/) (2026)
- [Decomposed Prototype Learning for Few-Shot Scene Graph Generation](https://mlsanity.com/paper/2303.10863/) (2023)
- [End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting](https://mlsanity.com/paper/2409.12499/) (2024)
- [From Data to Modeling: Fully Open-vocabulary Scene Graph Generation](https://mlsanity.com/paper/2505.20106/) (2025)
- [A Retrospect to Multi-prompt Learning across Vision and Language](https://mlsanity.com/paper/2511.00191/) (2025)
- [Predicate Debiasing in Vision-Language Models Integration for Scene Graph Generation Enhancement](https://mlsanity.com/paper/2403.16184/) (2024)
- [X-SG2S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks](https://mlsanity.com/paper/2502.10475/) (2025)
- [Measuring Image-Relation Alignment: Reference-Free Evaluation of VLMs and Synthetic Pre-training for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2509.01209/) (2025)

## Abstract (arXiv)

Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be given in the training set. Such a closed setting limits the practical application of SGG. In this paper, we introduce open-vocabulary scene graph generation, a novel, realistic and challenging setting in which a model is trained on a set of base object classes but is required to infer relations for unseen target object classes. To this end, we propose a two-step method that firstly pre-trains on large amounts of coarse-grained region-caption data and then leverages two prompt-based techniques to finetune the pre-trained model without updating its parameters. Moreover, our method can support inference over completely unseen object classes, which existing methods are incapable of handling. On extensive experiments on three benchmark datasets, Visual Genome, GQA, and Open-Image, our method significantly outperforms recent, strong SGG methods on the setting of Ov-SGG, as well as on the conventional closed SGG.