# Learning to Localize Temporal Events in Large-scale Video Data

arXiv: https://arxiv.org/abs/1910.11631 · HTML: https://mlsanity.com/paper/1910.11631/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance](https://mlsanity.com/paper/2501.06219/) (2025)

## Abstract (arXiv)

We address temporal localization of events in large-scale video data, in the context of the Youtube-8M Segments dataset. This emerging field within video recognition can enable applications to identify the precise time a specified event occurs in a video, which has broad implications for video search. To address this we present two separate approaches: (1) a gradient boosted decision tree model on a crafted dataset and (2) a combination of deep learning models based on frame-level data, video-level data, and a localization model. The combinations of these two approaches achieved 5th place in the 3rd Youtube-8M video recognition challenge.