Towards Good Practices for Video Object Segmentation
This work addresses video object segmentation for computer vision applications, but it is incremental as it focuses on refinements to existing methods.
The paper tackled semi-supervised video object segmentation by refining propagation-based methods, achieving an Overall score of 79.1 on the YouTube-VOS Challenge 2019 through an ablation study.
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.