Most activity localization methods in the literature suffer from the burden
of frame-wise annotation requirement. Learning from weak labels may be a
potential solution towards reducing such manual labeling effort. Recent years
have witnessed a substantial influx of tagged videos on the Internet, which can
serve as a rich source of weakly-supervised training data. Specifically, the
correlations between videos with similar tags can be utilized to temporally
localize the activities. Towards this goal, we present W-TALC, a
Weakly-supervised Temporal Activity Localization and Classification framework
using only video-level labels. The proposed network can be divided into two
sub-networks, namely the Two-Stream based feature extractor network and a
weakly-supervised module, which we learn by optimizing two complimentary loss
functions. Qualitative and quantitative results on two challenging datasets -
Thumos14 and ActivityNet1.2, demonstrate that the proposed method is able to
detect activities at a fine granularity and achieve better performance than
current state-of-the-art methods.

该论文提出了一种仅使用视频级标签的弱监督时序活动定位与分类框架，利用相关性来实现活动的时序定位，并在 Thumos14 和 ActivityNet1.2 数据集上实现了比当前最先进方法更好的性能。