  Semi-supervised video object segmentation is a fundamental yet Challenging
task in computer vision. Embedding matching based CFBI series networks have
achieved promising results by foreground-background integration approach.
Despite its superior performance, these works exhibit distinct shortcomings,
especially the false predictions caused by little appearance instances in first
frame, even they could easily be recognized by previous frame. Moreover, they
suffer from object's occlusion and error drifts. In order to overcome the
shortcomings , we propose Collaborative Attention Memory Network with an
enhanced segmentation head. We introduce a object context scheme that
explicitly enhances the object information, which aims at only gathering the
pixels that belong to the same category as a given pixel as its context.
Additionally, a segmentation head with Feature Pyramid Attention(FPA) module is
adopted to perform spatial pyramid attention structure on high-level output.
Furthermore, we propose an ensemble network to combine STM network with all
these new refined CFBI network. Finally, we evaluated our approach on the 2021
Youtube-VOS challenge where we obtain 6th place with an overall score of
83.5\%.
