UntrimmedNets for Weakly Supervised Action Recognition and Detection
Refereed conference paper presented and published in conference proceedings


摘要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.
著者Limin Wang, Yuanjun Xiong, Dahua Lin, Luc Van Gool
會議名稱IEEE Conference on Computer Vision and Pattern Recognition
會議地點Honolulu, Hawaii

上次更新時間 2018-20-01 於 18:58