# Recent Advances of Multimodal Continual Learning: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2410.05352 · HTML: https://mlsanity.com/paper/2410.05352/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 123 of its 205 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 30 of 123)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Grounded Language-Image Pre-training](https://mlsanity.com/paper/2112.03857/) (2021)

## Built on by (most cited first, 30 of 39)

- [Lifelong Learning of Large Language Model based Agents: A Roadmap](https://mlsanity.com/paper/2501.07278/) (2025)
- [Continual Multimodal Contrastive Learning](https://mlsanity.com/paper/2503.14963/) (2025)
- [Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting](https://mlsanity.com/paper/2508.04227/) (2025)
- [A Survey of Pathology Foundation Model: Progress and Future Directions](https://mlsanity.com/paper/2504.04045/) (2025)
- [Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network](https://mlsanity.com/paper/2412.15678/) (2024)
- [Recurrent Knowledge Identification and Fusion for Language Model Continual Learning](https://mlsanity.com/paper/2502.17510/) (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)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning](https://mlsanity.com/paper/2601.03938/) (2026)
- [TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents](https://mlsanity.com/paper/2602.21230/) (2026)
- [MOVER: Multimodal Optimal Transport with Volume-based Embedding Regularization](https://mlsanity.com/paper/2508.12149/) (2025)
- [Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models](https://mlsanity.com/paper/2512.03125/) (2025)
- [AIMMerging: Adaptive Iterative Model Merging Using Training Trajectories for Language Model Continual Learning](https://mlsanity.com/paper/2509.17348/) (2025)
- [Semi-supervised Node Importance Estimation with Informative Distribution Modeling for Uncertainty Regularization](https://mlsanity.com/paper/2503.20697/) (2025)
- [CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization](https://mlsanity.com/paper/2503.22740/) (2025)
- [Autoencoder-Based Hybrid Replay for Class-Incremental Learning](https://mlsanity.com/paper/2505.05926/) (2025)
- [MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning](https://mlsanity.com/paper/2504.07288/) (2025)
- [Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework](https://mlsanity.com/paper/2507.08459/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [MLLM-CTBench: A Benchmark for Continual Instruction Tuning with Reasoning Process Diagnosis](https://mlsanity.com/paper/2508.08275/) (2025)
- [Multimodal Policy Internalization for Conversational Agents](https://mlsanity.com/paper/2510.09474/) (2025)
- [Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM](https://mlsanity.com/paper/2605.05927/) (2026)
- [CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning](https://mlsanity.com/paper/2605.20247/) (2026)
- [RL Forgets! Towards Continual Policy Optimization](https://mlsanity.com/paper/2607.04364/) (2026)
- [Hyperbolic Multimodal Continual Learning](https://mlsanity.com/paper/2608.09572/) (2026)
- [MM-Prompt: Cross-Modal Prompt Tuning for Continual Visual Question Answering](https://mlsanity.com/paper/2505.19455/) (2025)
- [Multi-Modal Continual Learning via Cross-Modality Adapters and Representation Alignment with Knowledge Preservation](https://mlsanity.com/paper/2511.06723/) (2025)
- [Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learning](https://mlsanity.com/paper/2511.13351/) (2025)
- [Towards Continuous Intelligence Growth: Self-Training, Continual Learning, and Dual-Scale Memory in SuperIntelliAgent](https://mlsanity.com/paper/2511.23436/) (2025)

## Abstract (arXiv)

Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As models have evolved from small to large pre-trained architectures, and from supporting unimodal to multimodal data, multimodal continual learning (MMCL) methods have recently emerged. The primary complexity of MMCL is that it extends beyond a simple stacking of unimodal CL methods. Such straightforward approaches often suffer from multimodal catastrophic forgetting, yielding unsatisfactory performance. In addition, MMCL introduces new challenges that unimodal CL methods fail to adequately address, including modality imbalance, complex modality interaction, high computational costs, and degradation of pre-trained zero-shot capability of multimodal backbones. In this work, we present the first comprehensive survey on MMCL. We provide essential background knowledge and MMCL settings, as well as a structured taxonomy of MMCL methods. We categorize MMCL methods into four categories, i.e., regularization-based, architecture-based, replay-based, and prompt-based methods, explaining their methodologies and highlighting their key innovations. Additionally, to prompt further research in this field, we summarize open MMCL datasets and benchmarks, provide an in-depth discussion, and discuss several promising future directions. We have also created a GitHub repository for indexing relevant MMCL papers and open resources available at https://github.com/LucyDYu/Awesome-Multimodal-Continual-Learning.