# Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency

arXiv: https://arxiv.org/abs/2405.12648 · HTML: https://mlsanity.com/paper/2405.12648/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 30 of 31)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [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)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [Learning to Compose Dynamic Tree Structures for Visual Contexts](https://mlsanity.com/paper/1812.01880/) (2018)
- [A Comprehensive Survey of Deep Learning for Image Captioning](https://mlsanity.com/paper/1810.04020/) (2018)
- [GPS-Net: Graph Property Sensing Network for Scene Graph Generation](https://mlsanity.com/paper/2003.12962/) (2020)
- [Knowledge-Embedded Routing Network for Scene Graph Generation](https://mlsanity.com/paper/1903.03326/) (2019)
- [Scene Graph Generation from Objects, Phrases and Region Captions](https://mlsanity.com/paper/1707.09700/) (2017)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition](https://mlsanity.com/paper/1908.07325/) (2019)
- [Graphical Contrastive Losses for Scene Graph Parsing](https://mlsanity.com/paper/1903.02728/) (2019)
- [CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2009.07526/) (2020)
- [The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation](https://mlsanity.com/paper/2206.03014/) (2022)
- [From General to Specific: Informative Scene Graph Generation via Balance Adjustment](https://mlsanity.com/paper/2108.13129/) (2021)
- [Prototype-based Embedding Network for Scene Graph Generation](https://mlsanity.com/paper/2303.07096/) (2023)
- [Structured Query-Based Image Retrieval Using Scene Graphs](https://mlsanity.com/paper/2005.06653/) (2020)
- [Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation](https://mlsanity.com/paper/2005.08230/) (2020)
- [RU-Net: Regularized Unrolling Network for Scene Graph Generation](https://mlsanity.com/paper/2205.01297/) (2022)
- [Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2204.02597/) (2022)
- [Devil's on the Edges: Selective Quad Attention for Scene Graph Generation](https://mlsanity.com/paper/2304.03495/) (2023)
- [Iterative Scene Graph Generation with Generative Transformers](https://mlsanity.com/paper/2211.16636/) (2022)
- [Vision Relation Transformer for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.09472/) (2023)
- [Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural Network](https://mlsanity.com/paper/2212.00443/) (2022)
- [Compositional Feature Augmentation for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.06712/) (2023)
- [Peer Learning for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2301.00146/) (2022)

## Built on by (most cited first, 4 of 4)

- [SceneRAG: Scene-level Retrieval-Augmented Generation for Video Understanding](https://mlsanity.com/paper/2506.07600/) (2025)
- [Open World Scene Graph Generation using Vision Language Models](https://mlsanity.com/paper/2506.08189/) (2025)
- [BCTR: Bidirectional Conditioning Transformer for Scene Graph Generation](https://mlsanity.com/paper/2407.18715/) (2024)
- [Learning Context-Conditioned Predicate Semantics via Prototype Feedback](https://mlsanity.com/paper/2605.29610/) (2026)

## Abstract (arXiv)

Scene graph generation (SGG) is an important task in image understanding because it represents the relationships between objects in an image as a graph structure, making it possible to understand the semantic relationships between objects intuitively. Previous SGG studies used a message-passing neural networks (MPNN) to update features, which can effectively reflect information about surrounding objects. However, these studies have failed to reflect the co-occurrence of objects during SGG generation. In addition, they only addressed the long-tail problem of the training dataset from the perspectives of sampling and learning methods. To address these two problems, we propose CooK, which reflects the Co-occurrence Knowledge between objects, and the learnable term frequency-inverse document frequency (TF-l-IDF) to solve the long-tail problem. We applied the proposed model to the SGG benchmark dataset, and the results showed a performance improvement of up to 3.8% compared with existing state-of-the-art models in SGGen subtask. The proposed method exhibits generalization ability from the results obtained, showing uniform performance improvement for all MPNN models.