DSS-Prompt: Dynamic-Static Synergistic Prompting for Few-Shot Class-Incremental Learning

• URL: http://arxiv.org/abs/2508.09785v1
• Date: Wed, 13 Aug 2025 13:10:18 GMT
• Title: DSS-Prompt: Dynamic-Static Synergistic Prompting for Few-Shot Class-Incremental Learning
• Authors: Linpu He, Yanan Li, Bingze Li, Elvis Han Cui, Donghui Wang,
• Abstract summary: We introduce DSS-Prompt, a simple yet effective approach that transforms the pre-trained Vision Transformer with minimal modifications.<n>We conduct extensive experiments on four benchmarks to validate the effectiveness of our DSS-Prompt.<n>We show that it consistently achieves better performance than existing approaches on all datasets.
• Score: 4.957021413601961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from large-scale pre-trained models with strong generalization ability has shown remarkable success in a wide range of downstream tasks recently, but it is still underexplored in the challenging few-shot class-incremental learning (FSCIL) task. It aims to continually learn new concepts from limited training samples without forgetting the old ones at the same time. In this paper, we introduce DSS-Prompt, a simple yet effective approach that transforms the pre-trained Vision Transformer with minimal modifications in the way of prompts into a strong FSCIL classifier. Concretely, we synergistically utilize two complementary types of prompts in each Transformer block: static prompts to bridge the domain gap between the pre-training and downstream datasets, thus enabling better adaption; and dynamic prompts to capture instance-aware semantics, thus enabling easy transfer from base to novel classes. Specially, to generate dynamic prompts, we leverage a pre-trained multi-modal model to extract input-related diverse semantics, thereby generating complementary input-aware prompts, and then adaptively adjust their importance across different layers. In this way, on top of the prompted visual embeddings, a simple prototype classifier can beat state-of-the-arts without further training on the incremental tasks. We conduct extensive experiments on four benchmarks to validate the effectiveness of our DSS-Prompt and show that it consistently achieves better performance than existing approaches on all datasets and can alleviate the catastrophic forgetting issue as well.

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