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This paper investigates the principles of embedding learning to tackle thechallenging semi-supervised video object segmentation. Unlike previouspractices that focus on exploring the embedding learning of foreground object(s), we consider background should be equally treated. Thus, we propose aCollaborative video object segmentation by Foreground-Background Integration(CFBI) approach. CFBI separates the feature embedding into the foregroundobject region and its corresponding background region, implicitly promotingthem to be more contrastive and improving the segmentation results accordingly.Moreover, CFBI performs both pixel-level matching processes and instance-levelattention mechanisms between the reference and the predicted sequence, makingCFBI robust to various object scales. Based on CFBI, we introduce a multi-scalematching structure and propose an Atrous Matching strategy, resulting in a morerobust and efficient framework, CFBI+. We conduct extensive experiments on twopopular benchmarks, i.e., DAVIS and YouTube-VOS. Without applying any simulateddata for pre-training, our CFBI+ achieves the performance (J&F) of 82.9% and82.8%, outperforming all the other state-of-the-art methods. Code:https://github.com/z-x-yang/CFBI.

Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration