# Exploring Feature Representation and Training strategies in Temporal Action Localization

arXiv: https://arxiv.org/abs/1905.10608 · HTML: https://mlsanity.com/paper/1905.10608/

## Key facts

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

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

- [Few-Shot Action Localization without Knowing Boundaries](https://mlsanity.com/paper/2106.04150/) (2021)

## Abstract (arXiv)

Temporal action localization has recently attracted significant interest in the Computer Vision community. However, despite the great progress, it is hard to identify which aspects of the proposed methods contribute most to the increase in localization performance. To address this issue, we conduct ablative experiments on feature extraction methods, fixed-size feature representation methods and training strategies, and report how each influences the overall performance. Based on our findings, we propose a two-stage detector that outperforms the state of the art in THUMOS14, achieving a mAP@tIoU=0.5 equal to 44.2%.