# On guiding video object segmentation

## TL;DR

This paper introduces a guided convolutional neural network approach for video object segmentation that leverages foreground masks from existing algorithms to improve segmentation accuracy in challenging environments.

## Contribution

It proposes a novel guided CNN framework that uses external foreground masks to enhance segmentation, combining color and motion features for better separation.

## Key findings

- Outperforms non-guided segmentation methods on DAVIS 2016 dataset.
- Effectively integrates color and optical flow features for segmentation.
- Achieves state-of-the-art results in challenging environments.

## Abstract

This paper presents a novel approach for segmenting moving objects in unconstrained environments using guided convolutional neural networks. This guiding process relies on foreground masks from independent algorithms (i.e. state-of-the-art algorithms) to implement an attention mechanism that incorporates the spatial location of foreground and background to compute their separated representations. Our approach initially extracts two kinds of features for each frame using colour and optical flow information. Such features are combined following a multiplicative scheme to benefit from their complementarity. These unified colour and motion features are later processed to obtain the separated foreground and background representations. Then, both independent representations are concatenated and decoded to perform foreground segmentation. Experiments conducted on the challenging DAVIS 2016 dataset demonstrate that our guided representations not only outperform non-guided, but also recent and top-performing video object segmentation algorithms.