论文标题
长期对具有周期一致性的活动的预期
Long-Term Anticipation of Activities with Cycle Consistency
论文作者
论文摘要
随着深度学习方法在分析视频中的活动中的成功，最近更多的关注专注于预测未来的活动。但是，预期的大多数工作要么分析部分观察到的活动，要么预测下一个动作类别。最近，已经提出了新的方法将预测范围扩展到将来的几分钟，并预计将来会有一系列未来的活动，包括其持续时间。尽管这些作品将观察到的序列的语义解释从期待任务中解释，但我们提出了一个直接从观察到的框架的特征中预期未来活动的框架，并以端到端的方式训练它。此外，我们通过预测预测的未来来预测过去的活动来引入周期一致性损失。我们的框架在两个数据集上实现了最新的结果：早餐数据集和50萨拉德。
With the success of deep learning methods in analyzing activities in videos, more attention has recently been focused towards anticipating future activities. However, most of the work on anticipation either analyzes a partially observed activity or predicts the next action class. Recently, new approaches have been proposed to extend the prediction horizon up to several minutes in the future and that anticipate a sequence of future activities including their durations. While these works decouple the semantic interpretation of the observed sequence from the anticipation task, we propose a framework for anticipating future activities directly from the features of the observed frames and train it in an end-to-end fashion. Furthermore, we introduce a cycle consistency loss over time by predicting the past activities given the predicted future. Our framework achieves state-of-the-art results on two datasets: the Breakfast dataset and 50Salads.
