Activity recognition is a challenging problem with many practical applications. In addition to the visual features, recent approaches have benefited from the use of context, e.g., inter-relationships among the activities and objects. However, these approaches require data to be labeled, entirely available beforehand, and not designed to be updated continuously, which make them unsuitable for surveillance applications. In contrast, we propose a continuous-learning framework for context-aware activity recognition from unlabeled video, which has two distinct advantages over existing methods. First, it employs a novel active-learning technique that not only exploits the informativeness of the individual activities but also utilizes their contextual information during query selection; this leads to significant reduction in expensive manual annotation effort. Second, the learned models can be adapted online as more data is available. We formulate a conditional random field model that encodes the context and devise an information-theoretic approach that utilizes entropy and mutual information of the nodes to compute the set of most informative queries, which are labeled by a human. These labels are combined with graphical inference techniques for incremental updates. We provide a theoretical formulation of the active learning framework with an analytic solution. Experiments on six challenging datasets demonstrate that our framework achieves superior performance with significantly less manual labeling.

中文翻译：

---

事件识别中主动学习的上下文感知查询选择


活动识别对于许多实际应用来说都是一个具有挑战性的问题。除了视觉特征之外，最近的方法还受益于上下文的使用，例如活动和对象之间的相互关系。然而，这些方法需要对数据进行标记、预先完全可用，并且不设计为持续更新，这使得它们不适合监控应用。相比之下，我们提出了一种连续学习框架，用于从未标记视频中进行上下文感知活动识别，与现有方法相比，它具有两个明显的优势。首先，它采用了一种新颖的主动学习技术，不仅利用了个体活动的信息量，而且还在查询选择过程中利用了它们的上下文信息；这会显着减少昂贵的手动注释工作。其次，随着更多数据的可用，学习的模型可以在线调整。我们制定了一个对上下文进行编码的条件随机场模型，并设计了一种信息论方法，该方法利用节点的熵和互信息来计算由人类标记的信息最丰富的查询集。这些标签与图形推理技术相结合以进行增量更新。我们提供了主动学习框架的理论表述和分析解决方案。对六个具有挑战性的数据集的实验表明，我们的框架通过显着减少手动标记实现了卓越的性能。