PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

• URL: http://arxiv.org/abs/2501.09352v1
• Date: Thu, 16 Jan 2025 08:04:04 GMT
• Title: PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning
• Authors: Xianghu Yue, Yiming Chen, Xueyi Zhang, Xiaoxue Gao, Mengling Feng, Mingrui Lao, Huiping Zhuang, Haizhou Li,
• Abstract summary: Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs. A critical challenge remains: the issue of missing modalities during incremental learning phases. We propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios.
• Score: 42.00851701431368
• License:
• Abstract: Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forgetting. While existing studies primarily focus on the integration and utilization of multi-modal information for MMCIL, a critical challenge remains: the issue of missing modalities during incremental learning phases. This oversight can exacerbate severe forgetting and significantly impair model performance. To bridge this gap, we propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios. Concretely, we devise modality-specific prompts to compensate for missing information, facilitating the model to maintain a holistic representation of the data. On this foundation, we reformulate the MMCIL problem into a Recursive Least-Squares task, delivering an analytical linear solution. Building upon these, PAL not only alleviates the inherent under-fitting limitation in analytic learning but also preserves the holistic representation of missing-modality data, achieving superior performance with less forgetting across various multi-modal incremental scenarios. Extensive experiments demonstrate that PAL significantly outperforms competitive methods across various datasets, including UPMC-Food101 and N24News, showcasing its robustness towards modality absence and its anti-forgetting ability to maintain high incremental accuracy.

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