AB  - 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.
AU  - Yang, Z
AU  - Wei, Y
AU  - Yang, Y
DA  - 2020/01/01
DO  - 10.1007/978-3-030-58558-7_20
EP  - 348
JO  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB  - Springer International Publishing
PY  - 2020/01/01
SP  - 332
TI  - Collaborative Video Object Segmentation by Foreground-Background Integration
VL  - 12350 LNCS
Y1  - 2020/01/01
Y2  - 2026/05/17
ER  -