In the weakly supervised temporal video grounding study, previous methods use
predetermined single Gaussian proposals which lack the ability to express
diverse events described by the sentence query. To enhance the expression
ability of a proposal, we propose a Gaussian mixture proposal (GMP) that can
depict arbitrary shapes by learning importance, centroid, and range of every
Gaussian in the mixture. In learning GMP, each Gaussian is not trained in a
feature space but is implemented over a temporal location. Thus the
conventional feature-based learning for Gaussian mixture model is not valid for
our case. In our special setting, to learn moderately coupled Gaussian mixture
capturing diverse events, we newly propose a pull-push learning scheme using
pulling and pushing losses, each of which plays an opposite role to the other.
The effects of components in our scheme are verified in-depth with extensive
ablation studies and the overall scheme achieves state-of-the-art performance.
Our code is available at this https URL

通过引入高斯混合建议 (GMP) 和拉推学习模式，我们提出了一种新的弱监督时序视频定位方法。我们的方法在包括各种重要性、质心和范围的每个高斯中学习，以达到对复杂事件的有效表达和各种情况下的良好性能。