# Symbolic Replay: Scene Graph as Prompt for Continual Learning on VQA Task

arXiv: https://arxiv.org/abs/2208.12037 · HTML: https://mlsanity.com/paper/2208.12037/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 48 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge](https://mlsanity.com/paper/1906.00067/) (2019)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Learning multiple visual domains with residual adapters](https://mlsanity.com/paper/1705.08045/) (2017)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets](https://mlsanity.com/paper/2202.00273/) (2022)
- [Expert Gate: Lifelong Learning with a Network of Experts](https://mlsanity.com/paper/1611.06194/) (2016)
- [Unbiased Scene Graph Generation from Biased Training](https://mlsanity.com/paper/2002.11949/) (2020)
- [LAMOL: LAnguage MOdeling for Lifelong Language Learning](https://mlsanity.com/paper/1909.03329/) (2019)
- [In Defense of Grid Features for Visual Question Answering](https://mlsanity.com/paper/2001.03615/) (2020)

## Built on by (most cited first, 30 of 35)

- [AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn](https://mlsanity.com/paper/2306.08640/) (2023)
- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning](https://mlsanity.com/paper/2308.12305/) (2023)
- [Recent Advances of Multimodal Continual Learning: A Comprehensive Survey](https://mlsanity.com/paper/2410.05352/) (2024)
- [OED: Towards One-stage End-to-End Dynamic Scene Graph Generation](https://mlsanity.com/paper/2405.16925/) (2024)
- [Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting](https://mlsanity.com/paper/2508.04227/) (2025)
- [Revisiting Distillation for Continual Learning on Visual Question Localized-Answering in Robotic Surgery](https://mlsanity.com/paper/2307.12045/) (2023)
- [HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation](https://mlsanity.com/paper/2403.12033/) (2024)
- [Empowering Large Language Model for Continual Video Question Answering with Collaborative Prompting](https://mlsanity.com/paper/2410.00771/) (2024)
- [Bisecle: Binding and Separation in Continual Learning for Video Language Understanding](https://mlsanity.com/paper/2507.00469/) (2025)
- [MCITlib: Multimodal Continual Instruction Tuning Library and Benchmark](https://mlsanity.com/paper/2508.07307/) (2025)
- [Continual Learning for Generative AI: From LLMs to MLLMs and Beyond](https://mlsanity.com/paper/2506.13045/) (2025)
- [Parameter-Efficient Continual Fine-Tuning: A Survey](https://mlsanity.com/paper/2504.13822/) (2025)
- [Learning to Learn: How to Continuously Teach Humans and Machines](https://mlsanity.com/paper/2211.15470/) (2022)
- [Negative Pre-aware for Noisy Cross-modal Matching](https://mlsanity.com/paper/2312.05777/) (2023)
- [LW2G: Learning Whether to Grow for Prompt-based Continual Learning](https://mlsanity.com/paper/2409.18860/) (2024)
- [Adaptive Visual Scene Understanding: Incremental Scene Graph Generation](https://mlsanity.com/paper/2310.01636/) (2023)
- [I2I: Initializing Adapters with Improvised Knowledge](https://mlsanity.com/paper/2304.02168/) (2023)
- [Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation](https://mlsanity.com/paper/2406.19297/) (2024)
- [CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering](https://mlsanity.com/paper/2211.10567/) (2022)
- [One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering](https://mlsanity.com/paper/2411.02210/) (2024)
- [VLM-Assisted Continual learning for Visual Question Answering in Self-Driving](https://mlsanity.com/paper/2502.00843/) (2025)
- [PRISM-0: A Predicate-Rich Scene Graph Generation Framework for Zero-Shot Open-Vocabulary Tasks](https://mlsanity.com/paper/2504.00844/) (2025)
- [Task Formulation Matters When Learning Continually: A Case Study in Visual Question Answering](https://mlsanity.com/paper/2210.00044/) (2022)
- [Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing Images](https://mlsanity.com/paper/2407.14242/) (2024)
- [CluMo: Cluster-based Modality Fusion Prompt for Continual Learning in Visual Question Answering](https://mlsanity.com/paper/2408.11742/) (2024)
- [Task-Core Memory Management and Consolidation for Long-term Continual Learning](https://mlsanity.com/paper/2505.09952/) (2025)
- [CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2603.02951/) (2026)
- [LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models](https://mlsanity.com/paper/2503.21227/) (2025)
- [On Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language Models](https://mlsanity.com/paper/2603.27481/) (2026)

## Abstract (arXiv)

VQA is an ambitious task aiming to answer any image-related question. However, in reality, it is hard to build such a system once for all since the needs of users are continuously updated, and the system has to implement new functions. Thus, Continual Learning (CL) ability is a must in developing advanced VQA systems. Recently, a pioneer work split a VQA dataset into disjoint answer sets to study this topic. However, CL on VQA involves not only the expansion of label sets (new Answer sets). It is crucial to study how to answer questions when deploying VQA systems to new environments (new Visual scenes) and how to answer questions requiring new functions (new Question types). Thus, we propose CLOVE, a benchmark for Continual Learning On Visual quEstion answering, which contains scene- and function-incremental settings for the two aforementioned CL scenarios. In terms of methodology, the main difference between CL on VQA and classification is that the former additionally involves expanding and preventing forgetting of reasoning mechanisms, while the latter focusing on class representation. Thus, we propose a real-data-free replay-based method tailored for CL on VQA, named Scene Graph as Prompt for Symbolic Replay. Using a piece of scene graph as a prompt, it replays pseudo scene graphs to represent the past images, along with correlated QA pairs. A unified VQA model is also proposed to utilize the current and replayed data to enhance its QA ability. Finally, experimental results reveal challenges in CLOVE and demonstrate the effectiveness of our method. The dataset and code will be available at https://github.com/showlab/CLVQA.