# Meta Spatio-Temporal Debiasing for Video Scene Graph Generation

arXiv: https://arxiv.org/abs/2207.11441 · HTML: https://mlsanity.com/paper/2207.11441/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 47 references that are in the map.
- First posted on arXiv 23 Jul 2022.

## Builds on (most cited first, 30 of 34)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [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)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [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)
- [Gradient Matching for Domain Generalization](https://mlsanity.com/paper/2104.09937/) (2021)
- [Action Genome: Actions as Composition of Spatio-temporal Scene Graphs](https://mlsanity.com/paper/1912.06992/) (2019)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)
- [Knowledge-Embedded Routing Network for Scene Graph Generation](https://mlsanity.com/paper/1903.03326/) (2019)
- [Sequence to Sequence -- Video to Text](https://mlsanity.com/paper/1505.00487/) (2015)
- [Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2104.00308/) (2021)
- [Energy-Based Learning for Scene Graph Generation](https://mlsanity.com/paper/2103.02221/) (2021)
- [Graphical Contrastive Losses for Scene Graph Parsing](https://mlsanity.com/paper/1903.02728/) (2019)
- [Spatial-Temporal Transformer for Dynamic Scene Graph Generation](https://mlsanity.com/paper/2107.12309/) (2021)
- [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 Meta Face Recognition in Unseen Domains](https://mlsanity.com/paper/2003.07733/) (2020)
- [Fully Convolutional Scene Graph Generation](https://mlsanity.com/paper/2103.16083/) (2021)
- [Target Adaptive Context Aggregation for Video Scene Graph Generation](https://mlsanity.com/paper/2108.08121/) (2021)
- [From General to Specific: Informative Scene Graph Generation via Balance Adjustment](https://mlsanity.com/paper/2108.13129/) (2021)
- [Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph](https://mlsanity.com/paper/1903.10547/) (2019)

## Built on by (most cited first, 20 of 20)

- [Number it: Temporal Grounding Videos like Flipping Manga](https://mlsanity.com/paper/2411.10332/) (2024)
- [Panoptic Video Scene Graph Generation](https://mlsanity.com/paper/2311.17058/) (2023)
- [Meta Compositional Referring Expression Segmentation](https://mlsanity.com/paper/2304.04415/) (2023)
- [Pair then Relation: Pair-Net for Panoptic Scene Graph Generation](https://mlsanity.com/paper/2307.08699/) (2023)
- [Cross-Modality Time-Variant Relation Learning for Generating Dynamic Scene Graphs](https://mlsanity.com/paper/2305.08522/) (2023)
- [HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation](https://mlsanity.com/paper/2403.12033/) (2024)
- [LASER: A Neuro-Symbolic Framework for Learning Spatial-Temporal Scene Graphs with Weak Supervision](https://mlsanity.com/paper/2304.07647/) (2023)
- [Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation](https://mlsanity.com/paper/2409.10262/) (2024)
- [SSGVS: Semantic Scene Graph-to-Video Synthesis](https://mlsanity.com/paper/2211.06119/) (2022)
- [Learning to Generalize without Bias for Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2502.20158/) (2025)
- [Improving the Reliability for Confidence Estimation](https://mlsanity.com/paper/2210.06776/) (2022)
- [FloCoDe: Unbiased Dynamic Scene Graph Generation with Temporal Consistency and Correlation Debiasing](https://mlsanity.com/paper/2310.16073/) (2023)
- [Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning](https://mlsanity.com/paper/2403.10107/) (2024)
- [Weakly Supervised Video Scene Graph Generation via Natural Language Supervision](https://mlsanity.com/paper/2502.15370/) (2025)
- [Taking A Closer Look at Visual Relation: Unbiased Video Scene Graph Generation with Decoupled Label Learning](https://mlsanity.com/paper/2303.13209/) (2023)
- [End-to-end Open-vocabulary Video Visual Relationship Detection using Multi-modal Prompting](https://mlsanity.com/paper/2409.12499/) (2024)
- [METOR: A Unified Framework for Mutual Enhancement of Objects and Relationships in Open-vocabulary Video Visual Relationship Detection](https://mlsanity.com/paper/2505.06663/) (2025)
- [Towards Debiasing Frame Length Bias in Text-Video Retrieval via Causal Intervention](https://mlsanity.com/paper/2309.09311/) (2023)
- [A transition towards virtual representations of visual scenes](https://mlsanity.com/paper/2410.07987/) (2024)
- [T-STAR: A Large-Scale Benchmark for Spatio-Temporal Panoptic Scene Graph Generation in Satellite Video](https://mlsanity.com/paper/2607.21228/) (2026)

## Abstract (arXiv)

Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video. However, due to the long-tailed training data in datasets, the generalization performance of existing VidSGG models can be affected by the spatio-temporal conditional bias problem. In this work, from the perspective of meta-learning, we propose a novel Meta Video Scene Graph Generation (MVSGG) framework to address such a bias problem. Specifically, to handle various types of spatio-temporal conditional biases, our framework first constructs a support set and a group of query sets from the training data, where the data distribution of each query set is different from that of the support set w.r.t. a type of conditional bias. Then, by performing a novel meta training and testing process to optimize the model to obtain good testing performance on these query sets after training on the support set, our framework can effectively guide the model to learn to well generalize against biases. Extensive experiments demonstrate the efficacy of our proposed framework.