Current action recognition methods heavily rely on trimmed videos for model
training. However, it is expensive and time-consuming to acquire a large-scale
trimmed video dataset. This paper presents a new weakly supervised
architecture, called UntrimmedNet, which is able to directly learn action
recognition models from untrimmed videos without the requirement of temporal
annotations of action instances. Our UntrimmedNet couples two important
components, the classification module and the selection module, to learn the
action models and reason about the temporal duration of action instances,
respectively. These two components are implemented with feed-forward networks,
and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit
the learned models for action recognition (WSR) and detection (WSD) on the
untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet
only employs weak supervision, our method achieves performance superior or
comparable to that of those strongly supervised approaches on these two
datasets.

本文介绍了一种新的弱监督架构，称为 UntrimmedNet，它可以直接从未剪辑的视频中学习动作识别模型，无需动作实例的时间注释。我们的 UntrimmedNet 通过两个重要组件实现，分类模块和选择模块，分别用于学习动作模型和推理动作实例的时间持续性。虽然我们的 UntrimmedNet 仅采用弱监督，但我们的方法在 THUMOS14 和 ActivityNet 两个数据集上的性能优于或可与那些强监督方法相媲美。