论文标题
使用特定特定特征和活动相关性的多标签活动识别
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations
论文作者
论文摘要
多标签活动识别旨在识别每个视频中同时或顺序执行的多个活动。最新的活动识别网络集中在单活性上，每次视频中仅假设一个活动。这些网络为所有活动提取共享功能，这些功能不是为多标签活动而设计的。我们引入了一种用于多标签活动识别的方法，该方法为每种活动提取独立的特征描述符并学习活动相关性。该结构可以端到端训练，并插入任何现有的网络结构进行视频分类。在四个多标签活动识别数据集上，我们的方法优于最先进的方法。为了更好地了解系统生成的特定活动特征，我们在Charades数据集中可视化了这些特定活动的功能。
Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that assume only one activity in each video. These networks extract shared features for all the activities, which are not designed for multi-label activities. We introduce an approach to multi-label activity recognition that extracts independent feature descriptors for each activity and learns activity correlations. This structure can be trained end-to-end and plugged into any existing network structures for video classification. Our method outperformed state-of-the-art approaches on four multi-label activity recognition datasets. To better understand the activity-specific features that the system generated, we visualized these activity-specific features in the Charades dataset.
