  This paper tackles the problem of video object segmentation. We are
specifically concerned with the task of segmenting all pixels of a target
object in all frames, given the annotation mask in the first frame. Even when
such annotation is available this remains a challenging problem because of the
changing appearance and shape of the object over time. In this paper, we tackle
this task by formulating it as a meta-learning problem, where the base learner
grasping the semantic scene understanding for a general type of objects, and
the meta learner quickly adapting the appearance of the target object with a
few examples. Our proposed meta-learning method uses a closed form optimizer,
the so-called "ridge regression", which has been shown to be conducive for fast
and better training convergence. Moreover, we propose a mechanism, named "block
splitting", to further speed up the training process as well as to reduce the
number of learning parameters. In comparison with the-state-of-the art methods,
our proposed framework achieves significant boost up in processing speed, while
having very competitive performance compared to the best performing methods on
the widely used datasets.
