IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting

• URL: http://arxiv.org/abs/2503.20612v1
• Date: Wed, 26 Mar 2025 14:59:23 GMT
• Title: IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting
• Authors: Hao Fu, Hanbin Zhao, Jiahua Dong, Chao Zhang, Hui Qian,
• Abstract summary: We tackle the challenge of optimizing prompt designs for diverse tasks in Multi-Domain Class-Incremental Learning (MCIL)<n>Our Instance-Aware Gated Prompting (IA-GP) module enhances adaptation to new tasks while mitigating forgetting.<n>Our Instance-Aware Class-Distribution-Driven Prompting (IA-CDDP) improves the task adaptation process by determining an accurate task-label-related confidence score for each instance.
• Score: 26.933544407933034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent pre-trained vision-language models (PT-VLMs) often face a Multi-Domain Class-Incremental Learning (MCIL) scenario in practice, where several classes and domains of multi-modal tasks are incrementally arrived. Without access to previously learned tasks and unseen tasks, memory-constrained MCIL suffers from forward and backward forgetting. To alleviate the above challenges, parameter-efficient fine-tuning techniques (PEFT), such as prompt tuning, are employed to adapt the PT-VLM to the diverse incrementally learned tasks. To achieve effective new task adaptation, existing methods only consider the effect of PEFT strategy selection, but neglect the influence of PEFT parameter setting (e.g., prompting). In this paper, we tackle the challenge of optimizing prompt designs for diverse tasks in MCIL and propose an Instance-Aware Prompting (IAP) framework. Specifically, our Instance-Aware Gated Prompting (IA-GP) module enhances adaptation to new tasks while mitigating forgetting by dynamically assigning prompts across transformer layers at the instance level. Our Instance-Aware Class-Distribution-Driven Prompting (IA-CDDP) improves the task adaptation process by determining an accurate task-label-related confidence score for each instance. Experimental evaluations across 11 datasets, using three performance metrics, demonstrate the effectiveness of our proposed method. Code can be found at https://github.com/FerdinandZJU/IAP.

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