Enhanced Continual Learning of Vision-Language Models with Model Fusion

• URL: http://arxiv.org/abs/2503.10705v2
• Date: Fri, 21 Mar 2025 09:15:37 GMT
• Title: Enhanced Continual Learning of Vision-Language Models with Model Fusion
• Authors: Haoyuan Gao, Zicong Zhang, Yuqi Wei, Linglan Zhao, Guilin Li, Yexin Li, Linghe Kong, Weiran Huang,
• Abstract summary: Vision-Language Models (VLMs) represent a breakthrough in artificial intelligence.<n>VLMs are susceptible to catastrophic forgetting when sequentially fine-tuned on multiple downstream tasks.<n>We propose Continual Decoupling-Unifying (ConDU), a novel approach, by introducing model fusion into continual learning.
• Score: 16.764069327701186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language Models (VLMs) represent a breakthrough in artificial intelligence by integrating visual and textual modalities to achieve impressive zero-shot capabilities. However, VLMs are susceptible to catastrophic forgetting when sequentially fine-tuned on multiple downstream tasks. Existing continual learning methods for VLMs often rely heavily on additional reference datasets, compromise zero-shot performance, or are limited to parameter-efficient fine-tuning scenarios. In this paper, we propose Continual Decoupling-Unifying (ConDU), a novel approach, by introducing model fusion into continual learning for VLMs. ConDU maintains a unified model along with task triggers and prototype sets, employing an iterative process of decoupling task-specific models for previous tasks and unifying them with the model for the newly learned task. Additionally, we introduce an inference strategy for zero-shot scenarios by aggregating predictions from multiple decoupled task-specific models. Extensive experiments across various settings show that ConDU achieves up to a 2\% improvement in average performance across all seen tasks compared to state-of-the-art baselines, while also enhancing zero-shot capabilities relative to the original VLM.

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