论文标题
在弱监督下，学会适应看不见的异常活动
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
论文作者
论文摘要
我们提出了一个元学习框架，用于视频中弱监督的异常检测，当只有视频级别的二进制标签可用时，检测器学会了有效地适应看不见的异常活动。我们的工作是由于现有方法遭受泛滥而遭受多种看不见的例子的事实的动机。我们声称，配备元学习方案的异常检测器通过将模型带到初始化点以更好地优化，从而减轻了限制。我们在两个具有挑战性的数据集（UCF-Crime and Shanghaitech）上评估了框架的性能。实验结果表明，我们的算法增强了在弱监督环境中定位不见异常事件的能力。除了技术贡献外，我们还执行UCF-Crime数据集中缺失标签的注释，并对我们的任务进行有效评估。
We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available. Our work is motivated by the fact that existing methods suffer from poor generalization to diverse unseen examples. We claim that an anomaly detector equipped with a meta-learning scheme alleviates the limitation by leading the model to an initialization point for better optimization. We evaluate the performance of our framework on two challenging datasets, UCF-Crime and ShanghaiTech. The experimental results demonstrate that our algorithm boosts the capability to localize unseen abnormal events in a weakly supervised setting. Besides the technical contributions, we perform the annotation of missing labels in the UCF-Crime dataset and make our task evaluated effectively.
