# Learning Situation Hyper-Graphs for Video Question Answering

arXiv: https://arxiv.org/abs/2304.08682 · HTML: https://mlsanity.com/paper/2304.08682/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 51 of its 67 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 30 of 51)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [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)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions](https://mlsanity.com/paper/2105.08276/) (2021)
- [ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering](https://mlsanity.com/paper/1906.02467/) (2019)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [CLEVRER: CoLlision Events for Video REpresentation and Reasoning](https://mlsanity.com/paper/1910.01442/) (2019)
- [Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling](https://mlsanity.com/paper/2102.06183/) (2021)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering](https://mlsanity.com/paper/1704.04497/) (2017)
- [The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision](https://mlsanity.com/paper/1904.12584/) (2019)
- [MERLOT: Multimodal Neural Script Knowledge Models](https://mlsanity.com/paper/2106.02636/) (2021)
- [MovieQA: Understanding Stories in Movies through Question-Answering](https://mlsanity.com/paper/1512.02902/) (2015)
- [Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding](https://mlsanity.com/paper/1810.02338/) (2018)
- [Just Ask: Learning to Answer Questions from Millions of Narrated Videos](https://mlsanity.com/paper/2012.00451/) (2020)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)
- [Pure Transformers are Powerful Graph Learners](https://mlsanity.com/paper/2207.02505/) (2022)
- [MERLOT Reserve: Neural Script Knowledge through Vision and Language and Sound](https://mlsanity.com/paper/2201.02639/) (2022)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Hierarchical Conditional Relation Networks for Video Question Answering](https://mlsanity.com/paper/2002.10698/) (2020)
- [Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering](https://mlsanity.com/paper/1904.04357/) (2019)

## Built on by (most cited first, 16 of 16)

- [Large Language Models are Visual Reasoning Coordinators](https://mlsanity.com/paper/2310.15166/) (2023)
- [HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding](https://mlsanity.com/paper/2312.03050/) (2023)
- [HyperGLM: HyperGraph for Video Scene Graph Generation and Anticipation](https://mlsanity.com/paper/2411.18042/) (2024)
- [VideoMultiAgents: A Multi-Agent Framework for Video Question Answering](https://mlsanity.com/paper/2504.20091/) (2025)
- [Neural-Symbolic VideoQA: Learning Compositional Spatio-Temporal Reasoning for Real-world Video Question Answering](https://mlsanity.com/paper/2404.04007/) (2024)
- [From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge](https://mlsanity.com/paper/2506.08553/) (2025)
- [SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation](https://mlsanity.com/paper/2504.05925/) (2025)
- [Bridging Vision Language Models and Symbolic Grounding for Video Question Answering](https://mlsanity.com/paper/2509.11862/) (2025)
- [Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels](https://mlsanity.com/paper/2403.14430/) (2024)
- [Graph it first! Enabling Reasoning on Long-form Egocentric Videos through Scene Graphs](https://mlsanity.com/paper/2606.25842/) (2026)
- [Leveraging Static Relationships for Intra-Type and Inter-Type Message Passing in Video Question Answering](https://mlsanity.com/paper/2504.02417/) (2025)
- [REVEAL: Relation-based Video Representation Learning for Video-Question-Answering](https://mlsanity.com/paper/2504.05463/) (2025)
- [GHR-VQA: Graph-guided Hierarchical Relational Reasoning for Video Question Answering](https://mlsanity.com/paper/2511.20201/) (2025)
- [Multi-Modal Scene Graph with Kolmogorov-Arnold Experts for Audio-Visual Question Answering](https://mlsanity.com/paper/2511.23304/) (2025)
- [Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video](https://mlsanity.com/paper/2604.17298/) (2026)
- [RTAformer: Relation-Transition-Aware Transformer for Video Scene Graph Generation](https://mlsanity.com/paper/2610.20091/) (2026)

## Abstract (arXiv)

Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hyper-graphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks.