# Expanding Scene Graph Boundaries: Fully Open-vocabulary Scene Graph Generation via Visual-Concept Alignment and Retention

arXiv: https://arxiv.org/abs/2311.10988 · HTML: https://mlsanity.com/paper/2311.10988/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 53 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 30 of 43)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)
- [RegionCLIP: Region-based Language-Image Pretraining](https://mlsanity.com/paper/2112.09106/) (2021)
- [Scaling Open-Vocabulary Image Segmentation with Image-Level Labels](https://mlsanity.com/paper/2112.12143/) (2021)
- [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)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [ActionCLIP: A New Paradigm for Video Action Recognition](https://mlsanity.com/paper/2109.08472/) (2021)
- [Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model](https://mlsanity.com/paper/2203.14940/) (2022)
- [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)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Knowledge-Embedded Routing Network for Scene Graph Generation](https://mlsanity.com/paper/1903.03326/) (2019)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [Graph-Structured Representations for Visual Question Answering](https://mlsanity.com/paper/1609.05600/) (2016)
- [Aligning Bag of Regions for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2302.13996/) (2023)
- [Visual Translation Embedding Network for Visual Relation Detection](https://mlsanity.com/paper/1702.08319/) (2017)
- [Energy-Based Learning for Scene Graph Generation](https://mlsanity.com/paper/2103.02221/) (2021)
- [SGTR: End-to-end Scene Graph Generation with Transformer](https://mlsanity.com/paper/2112.12970/) (2021)
- [Graphical Contrastive Losses for Scene Graph Parsing](https://mlsanity.com/paper/1903.02728/) (2019)

## Built on by (most cited first, 29 of 29)

- [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)
- [Graph is a Substrate Across Data Modalities](https://mlsanity.com/paper/2601.22384/) (2026)
- [Compile Scene Graphs with Reinforcement Learning](https://mlsanity.com/paper/2504.13617/) (2025)
- [Towards Scene Graph Anticipation](https://mlsanity.com/paper/2403.04899/) (2024)
- [GPT4SGG: Synthesizing Scene Graphs from Holistic and Region-specific Narratives](https://mlsanity.com/paper/2312.04314/) (2023)
- [Open World Scene Graph Generation using Vision Language Models](https://mlsanity.com/paper/2506.08189/) (2025)
- [Towards Unbiased and Robust Spatio-Temporal Scene Graph Generation and Anticipation](https://mlsanity.com/paper/2411.13059/) (2024)
- [What Makes a Scene ? Scene Graph-based Evaluation and Feedback for Controllable Generation](https://mlsanity.com/paper/2411.15435/) (2024)
- [Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene](https://mlsanity.com/paper/2503.15019/) (2025)
- [Robo-SGG: Exploiting Layout-Oriented Normalization and Restitution Can Improve Robust Scene Graph Generation](https://mlsanity.com/paper/2504.12606/) (2025)
- [From Data to Modeling: Fully Open-vocabulary Scene Graph Generation](https://mlsanity.com/paper/2505.20106/) (2025)
- [Object-Centric Framework for Video Moment Retrieval](https://mlsanity.com/paper/2512.18448/) (2025)
- [The Semantic Lifecycle in Embodied AI: Acquisition, Representation and Storage via Foundation Models](https://mlsanity.com/paper/2601.08876/) (2026)
- [FORESCENE: FOREcasting human activity via latent SCENE graphs diffusion](https://mlsanity.com/paper/2503.06182/) (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)
- [Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering](https://mlsanity.com/paper/2510.01483/) (2025)
- [ZING-3D: Zero-shot Incremental 3D Scene Graphs via Vision-Language Models](https://mlsanity.com/paper/2510.21069/) (2025)
- [Hallucinate, Ground, Repeat: A Framework for Generalized Visual Relationship Detection](https://mlsanity.com/paper/2506.05651/) (2025)
- [Interaction-Centric Knowledge Infusion and Transfer for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2511.05935/) (2025)
- [SGG-Rrm 3: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation](https://mlsanity.com/paper/2603.07961/) (2026)
- [SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2603.18443/) (2026)
- [Caption Generation for Dongba Paintings via Prompt Learning and Semantic Fusion](https://mlsanity.com/paper/2603.22946/) (2026)
- [Can We Build Scene Graphs, Not Classify Them? FlowSG: Progressive Image-Conditioned Scene Graph Generation with Flow Matching](https://mlsanity.com/paper/2604.18623/) (2026)
- [CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2604.22274/) (2026)
- [ReLIC-SGG: Relation Lattice Completion for Open-Vocabulary Scene Graph Generation](https://mlsanity.com/paper/2604.22546/) (2026)
- [SceneGraphGrounder: Zero-Shot 3D Visual Grounding via Structured Scene Graph Matching](https://mlsanity.com/paper/2605.21788/) (2026)
- [Modeling Scientific Experiment Scenes: Dataset and Model](https://mlsanity.com/paper/2608.02892/) (2026)
- [OvDSGG: End-to-End Open-Vocabulary Dynamic Scene Graph Generation](https://mlsanity.com/paper/2608.14835/) (2026)

## Abstract (arXiv)

Scene Graph Generation (SGG) offers a structured representation critical in many computer vision applications. Traditional SGG approaches, however, are limited by a closed-set assumption, restricting their ability to recognize only predefined object and relation categories. To overcome this, we categorize SGG scenarios into four distinct settings based on the node and edge: Closed-set SGG, Open Vocabulary (object) Detection-based SGG (OvD-SGG), Open Vocabulary Relation-based SGG (OvR-SGG), and Open Vocabulary Detection + Relationbased SGG (OvD+R-SGG). While object-centric open vocabulary SGG has been studied recently, the more challenging problem of relation-involved open-vocabulary SGG remains relatively unexplored. To fill this gap, we propose a unified framework named OvSGTR towards fully open vocabulary SGG from a holistic view. The proposed framework is an end-to-end transformer architecture, which learns a visual-concept alignment for both nodes and edges, enabling the model to recognize unseen categories. For the more challenging settings of relation-involved open vocabulary SGG, the proposed approach integrates relation-aware pretraining utilizing image-caption data and retains visual-concept alignment through knowledge distillation. Comprehensive experimental results on the Visual Genome benchmark demonstrate the effectiveness and superiority of the proposed framework. Our code is available at https://github.com/gpt4vision/OvSGTR/.