提出轻量框架P2L-CA，无记忆缓冲实现高效多标签增量学习。
P2L-CA: An Effective Parameter Tuning Framework for Rehearsal-Free Multi-Label Class-Incremental Learning
- 用提示词与连续适配器解耦多标签特征，提升语义视觉对齐
- 在MS-COCO和PASCAL VOC上性能超越当前最优方法
- 参数极少且无需存储旧数据，适合资源受限场景
多标签类增量学习旨在持续识别复杂场景中同时出现的多个新类别。现有方法常因全参数微调带来高计算开销，或依赖记忆缓冲导致存储压力，且难以有效缓解特征混淆与领域差异问题。为此，本文提出P2L-CA框架，融合提示词转标签（Prompt-to-Label）模块与连续适配器（Continuous Adapter）模块。P2L模块利用类特定提示词分离多标签表征，并引入语言先验以强化语义-视觉对齐稳定性；CA模块通过轻量适配器缓解预训练模型与下游任务间的领域差距，增强模型可塑性。在标准与挑战性MLCIL设置下，基于MS-COCO和PASCAL VOC的大量实验表明，P2L-CA不仅显著优于当前最先进方法，且在类增量学习场景中展现出强泛化能力，同时仅需极少可训练参数，并完全无需记忆缓冲。
原文摘要 · Abstract (English)
Multi-label Class-Incremental Learning aims to continuously recognize novel categories in complex scenes where multiple objects co-occur. However, existing approaches often incur high computational costs due to full-parameter fine-tuning and substantial storage overhead from memory buffers, or they struggle to address feature confusion and domain discrepancies adequately. To overcome these limitations, we introduce P2L-CA, a parameter-efficient framework that integrates a Prompt-to-Label module with a Continuous Adapter module. The P2L module leverages class-specific prompts to disentangle multi-label representations while incorporating linguistic priors to enforce stable semantic-visual alignment. Meanwhile, the CA module employs lightweight adapters to mitigate domain gaps between pre-trained models and downstream tasks, thereby enhancing model plasticity. Extensive experiments across standard and challenging MLCIL settings on MS-COCO and PASCAL VOC show that P2L-CA not only achieves substantial improvements over state-of-the-art methods but also demonstrates strong generalization in CIL scenarios, all while requiring minimal trainable parameters and eliminating the need for memory buffers.
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