# Generative Multi-modal Models are Good Class-Incremental Learners

arXiv: https://arxiv.org/abs/2403.18383 · HTML: https://mlsanity.com/paper/2403.18383/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 74 of its 87 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 30 of 74)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [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)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [VideoBERT: A Joint Model for Video and Language Representation Learning](https://mlsanity.com/paper/1904.01766/) (2019)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Multimodal Few-Shot Learning with Frozen Language Models](https://mlsanity.com/paper/2106.13884/) (2021)
- [DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning](https://mlsanity.com/paper/2204.04799/) (2022)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)

## Built on by (most cited first, 22 of 22)

- [Continual Learning of Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2404.16789/) (2024)
- [Recent Advances of Foundation Language Models-based Continual Learning: A Survey](https://mlsanity.com/paper/2405.18653/) (2024)
- [Recent Advances of Multimodal Continual Learning: A Comprehensive Survey](https://mlsanity.com/paper/2410.05352/) (2024)
- [Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting](https://mlsanity.com/paper/2508.04227/) (2025)
- [Xiaomi MiMo-VL-Miloco Technical Report](https://mlsanity.com/paper/2512.17436/) (2025)
- [PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning](https://mlsanity.com/paper/2407.14029/) (2024)
- [MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware Classification](https://mlsanity.com/paper/2501.01110/) (2025)
- [When Large Multimodal Models Confront Evolving Knowledge: Challenges and Explorations](https://mlsanity.com/paper/2505.24449/) (2025)
- [Towards Incremental Learning in Large Language Models: A Critical Review](https://mlsanity.com/paper/2404.18311/) (2024)
- [One VLM to Keep it Learning: Generation and Balancing for Data-free Continual Visual Question Answering](https://mlsanity.com/paper/2411.02210/) (2024)
- [Modality-Inconsistent Continual Learning of Multimodal Large Language Models](https://mlsanity.com/paper/2412.13050/) (2024)
- [CLoG: Benchmarking Continual Learning of Image Generation Models](https://mlsanity.com/paper/2406.04584/) (2024)
- [Instruction-Grounded Visual Projectors for Continual Learning of Generative Vision-Language Models](https://mlsanity.com/paper/2508.00260/) (2025)
- [Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor](https://mlsanity.com/paper/2410.09237/) (2024)
- [Exemplar Masking for Multimodal Incremental Learning](https://mlsanity.com/paper/2412.09549/) (2024)
- [Knowledge Graph Enhanced Generative Multi-modal Models for Class-Incremental Learning](https://mlsanity.com/paper/2503.18403/) (2025)
- [Forward-Only Continual Learning](https://mlsanity.com/paper/2509.01533/) (2025)
- [Which Concepts to Forget and How to Refuse? Decomposing Concepts for Continual Unlearning in Large Vision-Language Models](https://mlsanity.com/paper/2603.21484/) (2026)
- [If you can describe it, they can see it: Cross-Modal Learning of Visual Concepts from Textual Descriptions](https://mlsanity.com/paper/2411.15611/) (2024)
- [MCIGLE: Multimodal Exemplar-Free Class-Incremental Graph Learning](https://mlsanity.com/paper/2509.06219/) (2025)
- [KORE: Enhancing Knowledge Injection for Large Multimodal Models via Knowledge-Oriented Controls](https://mlsanity.com/paper/2510.19316/) (2025)
- [Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning](https://mlsanity.com/paper/2603.21708/) (2026)

## Abstract (arXiv)

In class-incremental learning (CIL) scenarios, the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models, we would explore replacing discriminative models with generative ones for CIL. However, transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally, it requires formulating the task of CIL within a generative framework. To this end, we propose a novel generative multi-modal model (GMM) framework for class-incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text, we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings, we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting, we have improved by at least 14% accuracy over all the current state-of-the-art methods with significantly less forgetting. Our code is available at urlhttps://github.com/DoubleClass/GMM.