# Dynamic Face Video Segmentation via Reinforcement Learning

## TL;DR

This paper introduces a reinforcement learning approach for online key-frame decision in real-time face video segmentation, outperforming baselines and generalizing to other datasets.

## Contribution

It is the first to apply reinforcement learning to online key-frame decision in dynamic video segmentation and to focus on face videos.

## Key findings

- Reinforcement learning-based scheduler outperforms baselines in key selection and speed.
- Method generalizes well to Cityscapes dataset.
- First application of RL in face video segmentation.

## Abstract

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.