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Home
Dual-modality Adaptation In Vision-language Models For Continual Learning.

Home
Dual-modality Adaptation In Vision-language Models For Continual Learning.

Related Experiment Video

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Dual-modality adaptation in vision-language models for continual learning.

Jiayang Zeng¹, Wentao Zhang², Kanghao Chen³

¹School of Computer Science and Engineering, Sun Yat-sen Univerisity, Guangzhou, China; Department of Network Intelligence, Pengcheng Laboratory, Shenzhen, China; Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 18, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a new continual learning framework for vision-language models (VLM). It enhances knowledge acquisition and prevents forgetting by adapting both image and text modalities, outperforming existing methods.

Keywords:

Class-incremental learning Continual learning Vision-language model

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Area of Science:

Artificial Intelligence
Computer Vision
Natural Language Processing

Background:

Continual learning enables deep learning models to acquire new knowledge without forgetting previous information.
Vision-language models (VLMs) are increasingly explored for continual learning.
Current VLM continual learning methods often freeze one modality, limiting performance.

Purpose of the Study:

To propose a novel continual learning framework for VLMs that fully leverages both image and text modalities.
To overcome the limitations of single-modality adaptation in existing VLM continual learning approaches.

Main Methods:

Implemented a dual-modality adaptation strategy using task-specific LoRA modules for the image encoder and class-specific learnable text prompts for the text encoder.

Enhanced intra-class cohesion in image features with LoRA modules.

Improved inter-class feature separation using learnable text prompts and a prompt-reuse training strategy.

Main Results:

The proposed method significantly outperforms state-of-the-art approaches on multiple datasets.
Demonstrated effective knowledge acquisition and catastrophic forgetting mitigation in continual learning scenarios.
Showcased the benefits of dual-modality adaptation in VLMs.

Conclusions:

The dual-modality adaptation framework effectively exploits the potential of large-scale pre-trained VLMs for continual learning.
The proposed method offers a promising direction for advancing continual learning in multimodal AI.
Future work will involve releasing the code for public access and further research.