裁剪并重放：一种简单而通用的多标签在线持续学习策略
Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
AI总结：
针对多标签在线持续学习中的遗忘、缺失标签和类别不平衡问题，提出CUTER策略，通过裁剪标签特定区域进行经验重放，提供细粒度监督，与现有方法兼容并显著提升性能。
摘要：
多标签在线持续学习（MOCL）要求模型从无尽的多标签数据流中持续学习，面临包括持续灾难性遗忘、潜在缺失标签以及不可控的不平衡类别分布在内的复杂挑战。尽管现有的MOCL方法试图通过各种技术解决这些挑战，但它们都忽略了标签特定区域的识别和特征学习——这是根植于多标签学习的基本解决方案，但在具有增量和部分监督的在线设置中难以实现。为此，我们首先利用输入数据的固有结构信息来评估和验证不同预训练模型的先天定位能力。然后，我们提出CUTER（裁剪并经验重放），一种简单而通用的策略，通过进一步识别、加强和裁剪标签特定区域以进行高效经验重放，提供细粒度的监督信号。它不仅使模型能够同时解决灾难性遗忘、缺失标签和类别不平衡挑战，而且作为一种正交解决方案，可与现有方法无缝集成。在多个多标签图像基准上的大量实验证明了我们提出的方法的优越性。代码可在https://github.com/wxr99/Cut-Replay获取。
英文摘要：
Multi-Label Online Continual Learning (MOCL) requires models to learn continuously from endless multi-label data streams, facing complex challenges including persistent catastrophic forgetting, potential missing labels, and uncontrollable imbalanced class distributions. While existing MOCL methods attempt to address these challenges through various techniques, \textit{they all overlook label-specific region identifying and feature learning} - a fundamental solution rooted in multi-label learning but challenging to achieve in the online setting with incremental and partial supervision. To this end, we first leverage the inherent structural information of input data to evaluate and verify the innate localization capability of different pre-trained models. Then, we propose CUTER (CUT-out-and-Experience-Replay), a simple yet versatile strategy that provides fine-grained supervision signals by further identifying, strengthening and cutting out label-specific regions for efficient experience replay. It not only enables models to simultaneously address catastrophic forgetting, missing labels, and class imbalance challenges, but also serves as an orthogonal solution that seamlessly integrates with existing approaches. Extensive experiments on multiple multi-label image benchmarks demonstrate the superiority of our proposed method. The code is available at \href{https://github.com/wxr99/Cut-Replay}{https://github.com/wxr99/Cut-Replay}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
