# An Empirical Study of Propagation-based Methods for Video Object Segmentation

arXiv: https://arxiv.org/abs/1907.12769 · HTML: https://mlsanity.com/paper/1907.12769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

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

- [Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation](https://mlsanity.com/paper/2106.03330/) (2021)

## Abstract (arXiv)

While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings. In this paper, we carry out an empirical study for propagation-based methods. We view these approaches from a unified perspective and conduct detailed ablation study for core methods, input cues, multi-object combination and training strategies. With careful designs, our improved end-to-end memory networks achieve a global mean of 76.1 on DAVIS 2017 val set.