# Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation

arXiv: https://arxiv.org/abs/2203.09811 · HTML: https://mlsanity.com/paper/2203.09811/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 52 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [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)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [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)
- [Deep Modular Co-Attention Networks for Visual Question Answering](https://mlsanity.com/paper/1906.10770/) (2019)
- [Visual Relationship Detection with Language Priors](https://mlsanity.com/paper/1608.00187/) (2016)
- [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)
- [Graph-Structured Representations for Visual Question Answering](https://mlsanity.com/paper/1609.05600/) (2016)
- [Visual Translation Embedding Network for Visual Relation Detection](https://mlsanity.com/paper/1702.08319/) (2017)
- [Detecting Visual Relationships with Deep Relational Networks](https://mlsanity.com/paper/1704.03114/) (2017)
- [Say As You Wish: Fine-grained Control of Image Caption Generation with Abstract Scene Graphs](https://mlsanity.com/paper/2003.00387/) (2020)
- [Bridging Knowledge Graphs to Generate Scene Graphs](https://mlsanity.com/paper/2001.02314/) (2020)
- [Unpaired Image Captioning via Scene Graph Alignments](https://mlsanity.com/paper/1903.10658/) (2019)
- [Semantic Image Manipulation Using Scene Graphs](https://mlsanity.com/paper/2004.03677/) (2020)
- [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)
- [CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2009.07526/) (2020)
- [Learning to Segment the Tail](https://mlsanity.com/paper/2004.00900/) (2020)
- [Comprehensive Image Captioning via Scene Graph Decomposition](https://mlsanity.com/paper/2007.11731/) (2020)
- [Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study](https://mlsanity.com/paper/2104.00676/) (2021)

## Built on by (most cited first, 30 of 67)

- [SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding](https://mlsanity.com/paper/2406.10100/) (2024)
- [Prototype-based Embedding Network for Scene Graph Generation](https://mlsanity.com/paper/2303.07096/) (2023)
- [Visually-Prompted Language Model for Fine-Grained Scene Graph Generation in an Open World](https://mlsanity.com/paper/2303.13233/) (2023)
- [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)
- [LLM4SGG: Large Language Models for Weakly Supervised Scene Graph Generation](https://mlsanity.com/paper/2310.10404/) (2023)
- [Zero-shot Visual Relation Detection via Composite Visual Cues from Large Language Models](https://mlsanity.com/paper/2305.12476/) (2023)
- [Unbiased Scene Graph Generation via Two-stage Causal Modeling](https://mlsanity.com/paper/2307.05276/) (2023)
- [NICEST: Noisy Label Correction and Training for Robust Scene Graph Generation](https://mlsanity.com/paper/2207.13316/) (2022)
- [HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation](https://mlsanity.com/paper/2303.15994/) (2023)
- [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)
- [OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models](https://mlsanity.com/paper/2407.11213/) (2024)
- [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)
- [Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2208.03763/) (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)
- [VLPrompt: Vision-Language Prompting for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2311.16492/) (2023)
- [Adaptive Self-training Framework for Fine-grained Scene Graph Generation](https://mlsanity.com/paper/2401.09786/) (2024)
- [DSGG: Dense Relation Transformer for an End-to-end Scene Graph Generation](https://mlsanity.com/paper/2403.14886/) (2024)
- [Groupwise Query Specialization and Quality-Aware Multi-Assignment for Transformer-based Visual Relationship Detection](https://mlsanity.com/paper/2403.17709/) (2024)
- [DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation](https://mlsanity.com/paper/2503.13957/) (2025)
- [Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2207.07913/) (2022)
- [Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation](https://mlsanity.com/paper/2409.10262/) (2024)
- [Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation](https://mlsanity.com/paper/2308.03282/) (2023)
- [DeiSAM: Segment Anything with Deictic Prompting](https://mlsanity.com/paper/2402.14123/) (2024)
- [Temporal Sentence Grounding in Streaming Videos](https://mlsanity.com/paper/2308.07102/) (2023)
- [Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes](https://mlsanity.com/paper/2309.02286/) (2023)
- [Leveraging Predicate and Triplet Learning for Scene Graph Generation](https://mlsanity.com/paper/2406.02038/) (2024)
- [Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation](https://mlsanity.com/paper/2503.13947/) (2025)

## Abstract (arXiv)

Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also fail to provide informative predicates due to the biased relationship predictions, leading SGG far from practical. Towards this end, in this paper, we first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the inter-modal interaction, to serve as the encoder. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder. Particularly, based upon the observation that the recognition capability of one classifier is limited towards an extremely unbalanced dataset, we first deploy a group of classifiers that are expert in distinguishing different subsets of classes, and then cooperatively optimize them from two aspects to promote the unbiased SGG. Experiments conducted on VG and GQA datasets demonstrate that, we not only establish a new state-of-the-art in the unbiased metric, but also nearly double the performance compared with two baselines.