English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the weakly supervised temporal video grounding study, previous methods usepredetermined single Gaussian proposals which lack the ability to expressdiverse events described by the sentence query. To enhance the expressionability of a proposal, we propose a Gaussian mixture proposal (GMP) that candepict arbitrary shapes by learning importance, centroid, and range of everyGaussian in the mixture. In learning GMP, each Gaussian is not trained in afeature space but is implemented over a temporal location. Thus theconventional feature-based learning for Gaussian mixture model is not valid forour case. In our special setting, to learn moderately coupled Gaussian mixturecapturing diverse events, we newly propose a pull-push learning scheme usingpulling 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 extensiveablation studies and the overall scheme achieves state-of-the-art performance.Our code is available at https://github.com/sunoh-kim/pps.

Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding