Feature Calibration enhanced Parameter Synthesis for CLIP-based Class-incremental Learning

• URL: http://arxiv.org/abs/2503.18672v4
• Date: Thu, 17 Apr 2025 12:26:16 GMT
• Title: Feature Calibration enhanced Parameter Synthesis for CLIP-based Class-incremental Learning
• Authors: Juncen Guo, Yang Liu, Xiaoguang Zhu, Lianlong Sun, Liangyu Teng, Jingyi Wu, Di Li, Wei Zhou, Liang Song,
• Abstract summary: Class-Incremental Learning (CIL) enables models to continuously learn new class knowledge while retaining previous classes.<n>Traditional CIL methods rely primarily on visual features, which limits their effectiveness in complex, multimodal scenarios.<n>We propose a Feature Enhanced Synthesis (FCPS) framework that mitigates catastrophic generalization while preserving the model's intrinsic generalization capability.
• Score: 10.253058594622017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-Incremental Learning (CIL) enables models to continuously learn new class knowledge while retaining previous classes, facilitating adaptation and evolution in dynamic, real-world environments. Traditional CIL methods primarily rely on visual features, which limits their effectiveness in complex, multimodal scenarios. In contrast, VLMs show promising potential for enhancing CIL by leveraging pre-trained knowledge and integrating multi-modal semantic cues such as text and vision. However, existing approaches struggle to mitigate catastrophic forgetting while preserving the generalization strengths of VLMs across diverse modalities. To address these challenges, we propose a Feature Calibration Enhanced Parameter Synthesis (FCPS) framework. Specifically, FCPS introduces a dynamic parameter adjustment mechanism that iteratively calibrates the contribution of original visual features to the final class decision, thus preserving the model's intrinsic generalization capability across modalities. Simultaneously, parameter integration enables effective knowledge transfer, maintaining a balance between acquiring new class representations and preserving old knowledge. Experimental results on popular benchmarks (e.g., CIFAR100 and ImageNet100) validate the superiority of the proposed method.

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