# LinkNet: Relational Embedding for Scene Graph

arXiv: https://arxiv.org/abs/1811.06410 · HTML: https://mlsanity.com/paper/1811.06410/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 30 of 45)

- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [SGTR: End-to-end Scene Graph Generation with Transformer](https://mlsanity.com/paper/2112.12970/) (2021)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Learning of Visual Relations: The Devil is in the Tails](https://mlsanity.com/paper/2108.09668/) (2021)
- [Fully Convolutional Scene Graph Generation](https://mlsanity.com/paper/2103.16083/) (2021)
- [EGTR: Extracting Graph from Transformer for Scene Graph Generation](https://mlsanity.com/paper/2404.02072/) (2024)
- [VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud](https://mlsanity.com/paper/2303.14408/) (2023)
- [Devil's on the Edges: Selective Quad Attention for Scene Graph Generation](https://mlsanity.com/paper/2304.03495/) (2023)
- [SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level Scene Reconstruction](https://mlsanity.com/paper/2309.15702/) (2023)
- [Vision Relation Transformer for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.09472/) (2023)
- [Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs](https://mlsanity.com/paper/2305.08522/) (2023)
- [ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection](https://mlsanity.com/paper/2109.04047/) (2021)
- [DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation](https://mlsanity.com/paper/2503.13957/) (2025)
- [Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation](https://mlsanity.com/paper/2409.10262/) (2024)
- [Reusing Attention for One-stage Lane Topology Understanding](https://mlsanity.com/paper/2507.17617/) (2025)
- [A Survey on Knowledge Graph-based Methods for Automated Driving](https://mlsanity.com/paper/2210.08119/) (2022)
- [Probabilistic Debiasing of Scene Graphs](https://mlsanity.com/paper/2211.06444/) (2022)
- [Leveraging Predicate and Triplet Learning for Scene Graph Generation](https://mlsanity.com/paper/2406.02038/) (2024)
- [2.5D Visual Relationship Detection](https://mlsanity.com/paper/2104.12727/) (2021)
- [Few-Shot Video Object Detection](https://mlsanity.com/paper/2104.14805/) (2021)
- [Single-Stage Visual Relationship Learning using Conditional Queries](https://mlsanity.com/paper/2306.05689/) (2023)
- [Neural Belief Propagation for Scene Graph Generation](https://mlsanity.com/paper/2112.05727/) (2021)
- [Contrastive Object Detection Using Knowledge Graph Embeddings](https://mlsanity.com/paper/2112.11366/) (2021)
- [Neural Message Passing for Visual Relationship Detection](https://mlsanity.com/paper/2208.04165/) (2022)
- [Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions](https://mlsanity.com/paper/2106.08543/) (2021)
- [Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2301.00351/) (2023)
- [Importance Weighted Structure Learning for Scene Graph Generation](https://mlsanity.com/paper/2205.07017/) (2022)
- [Explore Contextual Information for 3D Scene Graph Generation](https://mlsanity.com/paper/2210.06240/) (2022)
- [In Defense of Clip-based Video Relation Detection](https://mlsanity.com/paper/2307.08984/) (2023)

## Abstract (arXiv)

Objects and their relationships are critical contents for image understanding. A scene graph provides a structured description that captures these properties of an image. However, reasoning about the relationships between objects is very challenging and only a few recent works have attempted to solve the problem of generating a scene graph from an image. In this paper, we present a method that improves scene graph generation by explicitly modeling inter-dependency among the entire object instances. We design a simple and effective relational embedding module that enables our model to jointly represent connections among all related objects, rather than focus on an object in isolation. Our method significantly benefits the main part of the scene graph generation task: relationship classification. Using it on top of a basic Faster R-CNN, our model achieves state-of-the-art results on the Visual Genome benchmark. We further push the performance by introducing global context encoding module and geometrical layout encoding module. We validate our final model, LinkNet, through extensive ablation studies, demonstrating its efficacy in scene graph generation.