# Regularizing modality contribution drift in multimodal continual learning

arXiv: https://arxiv.org/abs/2607.27260 · HTML: https://mlsanity.com/paper/2607.27260/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 29 Jul 2026.

## Builds on (most cited first, 24 of 24)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [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)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [VGGSound: A Large-scale Audio-Visual Dataset](https://mlsanity.com/paper/2004.14368/) (2020)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [Balanced Multimodal Learning via On-the-fly Gradient Modulation](https://mlsanity.com/paper/2203.15332/) (2022)
- [Learning to Answer Questions in Dynamic Audio-Visual Scenarios](https://mlsanity.com/paper/2203.14072/) (2022)
- [PMR: Prototypical Modal Rebalance for Multimodal Learning](https://mlsanity.com/paper/2211.07089/) (2022)
- [Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework](https://mlsanity.com/paper/2302.12247/) (2023)
- [Boosting Multi-modal Model Performance with Adaptive Gradient Modulation](https://mlsanity.com/paper/2308.07686/) (2023)
- [MMPareto: Boosting Multimodal Learning with Innocent Unimodal Assistance](https://mlsanity.com/paper/2405.17730/) (2024)
- [Does my multimodal model learn cross-modal interactions? It's harder to tell than you might think!](https://mlsanity.com/paper/2010.06572/) (2020)
- [ReconBoost: Boosting Can Achieve Modality Reconcilement](https://mlsanity.com/paper/2405.09321/) (2024)
- [Enhancing multimodal cooperation via sample-level modality valuation](https://mlsanity.com/paper/2309.06255/) (2023)
- [Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning](https://mlsanity.com/paper/2303.14771/) (2023)
- [Audio-Visual Class-Incremental Learning](https://mlsanity.com/paper/2308.11073/) (2023)
- [Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning](https://mlsanity.com/paper/2301.10931/) (2023)
- [Class-Incremental Grouping Network for Continual Audio-Visual Learning](https://mlsanity.com/paper/2309.05281/) (2023)
- [Enhancing Continual Learning in Visual Question Answering with Modality-Aware Feature Distillation](https://mlsanity.com/paper/2406.19297/) (2024)
- [Continual Audio-Visual Sound Separation](https://mlsanity.com/paper/2411.02860/) (2024)

## Abstract (arXiv)

Multimodal continual learning (MMCL) aims to acquire new knowledge from multimodal data while retaining previously learned knowledge. Existing MMCL methods primarily mitigate forgetting by aligning cross-modal representations or preserving feature-level semantic similarity. However, different tasks may rely on different modalities, and learning new tasks can alter how modalities contribute to predictions on previously learned tasks. It remains underexplored how modality contributions evolve across incremental stages and how such changes relate to forgetting in MMCL. We term such changes Modality Contribution Drift (MCD) and introduce an MCD score based on controlled modality-subset interventions. Our theoretical and empirical analyses show how contribution drift can lead to forgetting, while existing MMCL and conventional CL methods do not effectively mitigate MCD. To address this issue, we propose Continual Modality Contribution Drift Regularization (CMCDR) to preserve the modality contribution profiles of previously learned tasks in both replay-based and replay-free settings. In the replay-based setting, CMCDR estimates modality contributions from stored old samples and regularizes their drift relative to a frozen previous model. In the replay-free setting, CMCDR uses current-task samples as probes to match old-class contribution profiles between the current and frozen models without storing old exemplars. Across six benchmarks covering multimodal class-incremental learning and continual multimodal question answering, CMCDR substantially reduces modality contribution drift, improves average accuracy by 0.88-7.66 percentage points, and reduces average forgetting by 0.85-11.55 percentage points over the corresponding baselines.