Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture
  Diverse Events for Weakly Supervised Temporal Video Grounding
Sunoh Kim Jungchan Cho Joonsang Yu YoungJoon Yoo Jin Young Choi
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 https://github.com/sunoh-kim/pps.