# Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation

arXiv: https://arxiv.org/abs/2201.06794 · HTML: https://mlsanity.com/paper/2201.06794/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Long-tail learning via logit adjustment](https://mlsanity.com/paper/2007.07314/) (2020)
- [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)
- [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)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [Gradient Harmonized Single-stage Detector](https://mlsanity.com/paper/1811.05181/) (2018)
- [Scene Graph Generation with External Knowledge and Image Reconstruction](https://mlsanity.com/paper/1904.00560/) (2019)
- [PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2009.00893/) (2020)
- [Recovering the Unbiased Scene Graphs from the Biased Ones](https://mlsanity.com/paper/2107.02112/) (2021)
- [Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation](https://mlsanity.com/paper/1707.09423/) (2017)
- [Comprehensive Image Captioning via Scene Graph Decomposition](https://mlsanity.com/paper/2007.11731/) (2020)
- [On Exploring Undetermined Relationships for Visual Relationship Detection](https://mlsanity.com/paper/1905.01595/) (2019)

## Built on by (most cited first, 22 of 22)

- [STAR: A First-Ever Dataset and A Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery](https://mlsanity.com/paper/2406.09410/) (2024)
- [Unbiased Scene Graph Generation via Two-stage Causal Modeling](https://mlsanity.com/paper/2307.05276/) (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)
- [Adaptive Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2207.04602/) (2022)
- [HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation](https://mlsanity.com/paper/2403.12033/) (2024)
- [A Causal Adjustment Module for Debiasing Scene Graph Generation](https://mlsanity.com/paper/2503.17862/) (2025)
- [Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2207.07913/) (2022)
- [Rethinking the Evaluation of Unbiased Scene Graph Generation](https://mlsanity.com/paper/2208.01909/) (2022)
- [3D Human Interaction Generation: A Survey](https://mlsanity.com/paper/2503.13120/) (2025)
- [AUG: A New Dataset and An Efficient Model for Aerial Image Urban Scene Graph Generation](https://mlsanity.com/paper/2404.07788/) (2024)
- [Peer Learning for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2301.00146/) (2022)
- [Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2208.07109/) (2022)
- [Generalized Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.04802/) (2023)
- [LANDMARK: Language-guided Representation Enhancement Framework for Scene Graph Generation](https://mlsanity.com/paper/2303.01080/) (2023)
- [RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation](https://mlsanity.com/paper/2309.03240/) (2023)
- [Zero-Shot Scene Graph Generation via Triplet Calibration and Reduction](https://mlsanity.com/paper/2309.03542/) (2023)
- [Fine-Grained Scene Graph Generation via Sample-Level Bias Prediction](https://mlsanity.com/paper/2407.19259/) (2024)
- [UniQ: Unified Decoder with Task-specific Queries for Efficient Scene Graph Generation](https://mlsanity.com/paper/2501.05687/) (2025)
- [A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation](https://mlsanity.com/paper/2505.23451/) (2025)
- [Salience-SGG: Enhancing Unbiased Scene Graph Generation with Iterative Salience Estimation](https://mlsanity.com/paper/2601.08728/) (2026)
- [Head-Tail Cooperative Learning Network for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2308.12048/) (2023)

## Abstract (arXiv)

Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. Specifically, RTPB uses a distributed-based prior bias to improve models' detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories. In addition, to further explore the contextual information of objects and relationships, we design a contextual encoding backbone network, termed as Dual Transformer (DTrans). We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. In specific, our RTPB achieves an improvement of over 10% under the mean recall when applied to current SGG methods. Furthermore, DTrans with RTPB outperforms nearly all state-of-the-art methods with a large margin.