课堂监控图像中活动识别的少样本持续学习
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images
AI总结：
针对真实课堂监控中正常教学活动样本多、非教学活动样本少且持续出现的问题，本文构建 ARIC 数据集，并提出结合 SCL 与 ACC 的少样本持续学习方法，取得优于现有方法的性能。
摘要：
活动识别在“AI+教育”领域的应用正受到越来越多的关注。然而，当前工作主要集中于识别人工采集视频中的活动以及有限数量的活动类型，很少关注真实课堂监控图像中的活动识别。在真实课堂环境中，阅读等正常教学活动占据样本的很大比例，而进食等罕见非教学活动会持续出现。这要求模型能够从少量样本中学习非教学活动，同时不遗忘正常教学活动，因此需要具备少样本持续学习（FSCL）能力。为弥补这一空白，我们构建了一个专注于课堂监控图像活动识别的持续学习数据集，名为 ARIC（Activity Recognition in Classroom，课堂活动识别）。该数据集具有多视角、活动种类丰富和真实场景等优势，但也带来活动相似、样本分布不均衡等挑战。为克服这些挑战，我们设计了一种结合监督对比学习（SCL）与自适应协方差分类器（ACC）的少样本持续学习方法。在基础阶段，我们提出一种基于特征增强的 SCL 方法，以提升模型的泛化能力；在增量阶段，我们采用 ACC 更准确地描述新类别的分布。实验结果表明，我们的方法在 ARIC 数据集上优于其他现有方法。
英文摘要：
The application of activity recognition in the "AI + Education" field is gaining increasing attention. However, current work mainly focuses on the recognition of activities in manually captured videos and a limited number of activity types, with little attention given to recognizing activities in surveillance images from real classrooms. In real classroom settings, normal teaching activities such as reading, account for a large proportion of samples, while rare non-teaching activities such as eating, continue to appear. This requires a model that can learn non-teaching activities from few samples without forgetting the normal teaching activities, which necessitates fewshot continual learning (FSCL) capability. To address this gap, we constructed a continual learning dataset focused on classroom surveillance image activity recognition called ARIC (Activity Recognition in Classroom). The dataset has advantages such as multiple perspectives, a wide variety of activities, and real-world scenarios, but it also presents challenges like similar activities and imbalanced sample distribution. To overcome these challenges, we designed a few-shot continual learning method that combines supervised contrastive learning (SCL) and an adaptive covariance classifier (ACC). During the base phase, we proposed a SCL approach based on feature augmentation to enhance the model's generalization ability. In the incremental phase, we employed an ACC to more accurately describe the distribution of new classes. Experimental results demonstrate that our method outperforms other existing methods on the ARIC dataset.
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。
