Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

1-2026

Abstract

Food analysis has become increasingly critical for health-related tasks such as personalized nutrition and chronic disease prevention. However, existing large multimodal models (LMMs) in food analysis suffer from catastrophic forgetting when learning new tasks, requiring costly retraining from scratch. To address this, we propose a novel continual learning framework for multimodal food learning, integrating a Dual-LoRA architecture with Quality-Enhanced Pseudo Replay. We introduce two complementary low-rank adapters for each task: a specialized LoRA that learns task-specific knowledge with orthogonal constraints to previous tasks’ subspaces, and a cooperative LoRA that consolidates shared knowledge across tasks via pseudo replay. To improve the reliability of replay data, our Quality-Enhanced Pseudo Replay strategy leverages self-consistency and semantic similarity to reduce hallucinations in generated samples. Experiments on the comprehensive Uni-Food dataset show superior performance in mitigating forgetting, representing the first effective continual learning approach for complex food tasks.

Keywords

Continual Learning, Food Analysis, Large Multimodal Models

Discipline

Databases and Information Systems | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Multimedia Modeling: 32nd International Conference on Multimedia Modeling, MMM 2026, Prague, Czech Republic, January 29-31, Proceedings

First Page

173

Last Page

187

ISBN

9789819569595

Identifier

10.1007/978-981-95-6960-1_13

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-981-95-6960-1_13

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