Can I read Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization on EtoBox?

Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization by Huang, Linjiang; Wang, Liang; Li, Hongsheng is a scholarly article available to read on EtoBox.

What is Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization about?

As a challenging task of high-level video understanding, weakly supervised temporal action localization has been attracting increasing attention. With only video annotations, most existing methods seek to handle this task with a localization-by-classification framework, which generally adopts a selector to select snippets of high probabilities of actions or namely the foreground. Nevertheless, the existing foreground selection strategies have a major limitation of only considering the unilateral relation from foreground to actions, which cannot guarantee the foreground-action consistency. In this paper, we present a framework named FAC-Net based on the I3D backbone, on which three branches are appended, named class-wise foreground classification branch, class-agnostic attention branch and multiple instance learning branch. First, our class-wise foreground classification branch regularizes the relation between actions and foreground to maximize the foreground-background separation. Besides, the class-agnostic attention branch and multiple instance learning branch are adopted to regularize the foreground-action consistency and help to learn a meaningful foreground classifier. Within

Author

Huang, Linjiang; Wang, Liang; Li, Hongsheng

Published

2021

Language

EN