Towards Good Practices for Video Object Segmentation

09/30/2019
by   Dongdong Yu, et al.
0

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.

READ FULL TEXT
research
06/24/2022

The Second Place Solution for The 4th Large-scale Video Object Segmentation Challenge–Track 3: Referring Video Object Segmentation

The referring video object segmentation task (RVOS) aims to segment obje...
research
07/30/2019

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

While propagation-based approaches have achieved state-of-the-art perfor...
research
06/27/2023

TrickVOS: A Bag of Tricks for Video Object Segmentation

Space-time memory (STM) network methods have been dominant in semi-super...
research
07/16/2020

Kernelized Memory Network for Video Object Segmentation

Semi-supervised video object segmentation (VOS) is a task that involves ...
research
10/23/2020

Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation

In this paper, we address several inadequacies of current video object s...
research
05/17/2022

Collaborative Attention Memory Network for Video Object Segmentation

Semi-supervised video object segmentation is a fundamental yet Challengi...
research
05/26/2020

ALBA : Reinforcement Learning for Video Object Segmentation

We consider the challenging problem of zero-shot video object segmentati...

Please sign up or login with your details

Forgot password? Click here to reset