Semantic Prompting with Image Token for Continual Learning

Jisu Han, Jaemin Na, Wonjun Hwang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6987-6997

Continual learning aims to refine model parameters for new tasks while retaining knowledge from previous tasks. Recently prompt-based learning has emerged to leverage pre-trained models to be prompted to learn subsequent tasks without the reliance on the rehearsal buffer. Although this approach has demonstrated outstanding results existing methods depend on preceding task-selection process to choose appropriate prompts. However imperfectness in task-selection may lead to negative impacts on the performance particularly in the scenarios where the number of tasks is large or task distributions are imbalanced. To address this issue we introduce a novel task-agnostic approach that focuses on the visual semantic information of image tokens eliminating the preceding task prediction. By leveraging the ability of the pre-trained model to discriminate between similar tokens our method not only subdivides the prompt but also eliminates the need for additional forward pass. Consequently we achieve competitive performance on four benchmarks while significantly reducing training time compared to state-of-the-art methods. The code is available at https://github.com/pilsHan/I-Prompt