Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models
Unified Multimodal Generative Models (UMGMs) unify visual understanding and image generation within a single autoregressive framework. However, their ability to continually learn new tasks is severely hindered by catastrophic forgetting, both within a modality (intra-modal) and across modalities (inter-modal). While intra-modal forgetting has been studied in prior continual learning (CL) work, inter-modal forgetting remains largely unexplored. In this paper, we identify and empirically validate this phenomenon in UMGMs and provide a theoretical explanation rooted in gradient conflict between modalities. To address both intra- and inter-modal forgetting, we propose Modality-Decoupled Experts (MoDE), a lightweight and scalable architecture that isolates modality-specific updates to mitigate the gradient conflict and leverages knowledge distillation to prevent catastrophic forgetting and preserve pre-trained capabilities. Unlike previous CL methods that remain modality-coupled and suffer from modality gradient conflict, MoDE explicitly decouples modalities to prevent interference. Experiments across diverse benchmarks demonstrate that MoDE significantly mitigates both inter- and intra-modal forgetting, outperforming prior CL baselines in unified multimodal generation settings. Codes will be publicly available: https://github.com/Christina200/MoDE-official.git
693314d93962c926cf680439
Xiwen Wei
claimed_verified
2025-12-08T08:31:21.988Z
65ce8199dc6fd929093c0489
/avatars/847fc64424fa8e619dbc70f607dc6e13.svg
Xiwen Wei
ChristinaW
user
ChristinaW
693314d93962c926cf68043a
Mustafa Munir
693314d93962c926cf68043b
Radu Marculescu
Mitigating Intra- and Inter-modal Forgetting in Continual Learning of Unified Multimodal Models
Unified Multimodal Generative Models (UMGMs) unify visual understanding and image generation within a single autoregressive framework. However, their ability to continually learn new tasks is severely hindered by catastrophic forgetting, both within a modality (intra-modal) and across modalities (inter-modal). While intra-modal forgetting has been studied in prior continual learning (CL) work, inter-modal forgetting remains largely unexplored. In this paper, we identify and empirically validate this phenomenon in UMGMs and provide a theoretical explanation rooted in gradient conflict between modalities. To address both intra- and inter-modal forgetting, we propose Modality-Decoupled Experts (MoDE), a lightweight and scalable architecture that isolates modality-specific updates to mitigate the gradient conflict and leverages knowledge distillation to prevent catastrophic forgetting and preserve pre-trained capabilities. Unlike previous CL methods that remain modality-coupled and suffer from modality gradient conflict, MoDE explicitly decouples modalities to prevent interference. Experiments across diverse benchmarks demonstrate that MoDE significantly mitigates both inter- and intra-modal forgetting, outperforming prior CL baselines in unified multimodal generation settings. Codes will be publicly available: https://github.com/Christina200/MoDE-official.git
Modality-Decoupled Experts (MoDE) mitigate intra-modal and inter-modal forgetting in Unified Multimodal Generative Models (UMGMs) through decoupled updates and knowledge distillation.
Unified Multimodal Generative Models UMGMs catastrophic forgetting intra-modal forgetting inter-modal forgetting continual learning CL autoregressive framework gradient conflict Modality-Decoupled Experts MoDE knowledge distillation