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.
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本论文研究了应用嵌入式学习原理来解决半监督视频对象分割的挑战。与以往只使用前景目标的像素探索嵌入学习不同，本文认为背景也应该得到同样的对待，因此提出了 CFBI 方法，通过前背景一起的特征嵌入来提高对象的分割效果。CFBI 利用了前后景的特征嵌入，从像素和实例级别的角度对参考和预测序列进行匹配，使得 CFBI 对各种对象尺度都具有鲁棒性。在三个流行的基准测试上，即 DAVIS 2016、DAVIS 2017 和 YouTube-VOS 上，CFBI 的表现分别为 89.4％，81.9％和 81.4％，超越了所有其他最先进的方法。