Collaborative Video Object Segmentation by Foreground-Background Integration

TL;DR

This paper introduces CFBI, a novel semi-supervised video object segmentation method that treats foreground and background features equally, enhancing segmentation robustness and outperforming existing state-of-the-art approaches on multiple benchmarks.

Contribution

The paper proposes a contrastive embedding learning approach that integrates foreground and background features for improved video object segmentation.

Findings

Achieves state-of-the-art performance on DAVIS 2016, DAVIS 2017, and YouTube-VOS datasets.

Effectively promotes contrastive feature embedding between foreground and background.

Demonstrates robustness to various object scales through combined pixel and instance-level matching.

Abstract

This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation. Different from previous practices that only explore the embedding learning using pixels from foreground object (s), we consider background should be equally treated and thus propose Collaborative video object segmentation by Foreground-Background Integration (CFBI) approach. Our CFBI implicitly imposes the feature embedding from the target foreground object and its corresponding background to be contrastive, promoting the segmentation results accordingly. With the feature embedding from both foreground and background, our CFBI performs the matching process between the reference and the predicted sequence from both pixel and instance levels, making the CFBI be robust to various object scales. We conduct extensive experiments on three popular benchmarks, i.e., DAVIS 2016, DAVIS 2017, and YouTube-VOS. Our CFBI achieves the performance (J$F) of 89.4%, 81.9%, and 81.4%, respectively, outperforming all the other state-of-the-art methods. Code: https://github.com/z-x-yang/CFBI.