# Weakly Supervised Gaussian Networks for Action Detection

## TL;DR

This paper introduces WSGN, a weakly supervised approach for action detection in videos that relies only on video-level labels, significantly reducing annotation costs while achieving competitive results.

## Contribution

The paper presents a novel weakly supervised method, WSGN, that effectively detects actions using only video-level labels, leveraging dataset-wide and video-specific statistics.

## Key findings

- Achieves state-of-the-art results on THUMOS14 with similar features and loss functions.
- Performs within 0.3% mAP of fully supervised methods on Charades.
- Demonstrates effective action detection with minimal supervision.

## Abstract

Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels. This expensive annotation process limits deploying action detectors to a limited number of categories. We propose a novel method, called WSGN, that learns to detect actions from \emph{weak supervision}, using only video-level labels. WSGN learns to exploit both video-specific and dataset-wide statistics to predict relevance of each frame to an action category. This strategy leads to significant gains in action detection for two standard benchmarks THUMOS14 and Charades. Our method obtains excellent results compared to state-of-the-art methods that uses similar features and loss functions on THUMOS14 dataset. Similarly, our weakly supervised method is only 0.3% mAP behind a state-of-the-art supervised method on challenging Charades dataset for action localization.