Towards Good Practices for Video Object Segmentation

TL;DR

This paper explores effective refinement techniques for semi-supervised video object segmentation, demonstrating significant performance improvements on the Youtube-VOS benchmark through empirical evaluation.

Contribution

It introduces a series of refinements to propagation-based segmentation methods and empirically evaluates their impact, leading to improved state-of-the-art results.

Findings

Achieved 79.1 overall score on Youtube-VOS 2019

Refinements significantly improve segmentation accuracy

Empirical ablation study validates each refinement's effectiveness

Abstract

Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empirically evaluate their impact on the final model performance through ablation study. By taking all the refinements, we improve the space-time memory networks to achieve a Overall of 79.1 on the Youtube-VOS Challenge 2019.