  The encoder-decoder based methods for semi-supervised video object
segmentation (Semi-VOS) have received extensive attention due to their superior
performances. However, most of them have complex intermediate networks which
generate strong specifiers to be robust against challenging scenarios, and this
is quite inefficient when dealing with relatively simple scenarios. To solve
this problem, we propose a real-time network, Clue Refining Network for Video
Object Segmentation (CRVOS), that does not have any intermediate network to
efficiently deal with these scenarios. In this work, we propose a simple
specifier, referred to as the Clue, which consists of the previous frame's
coarse mask and coordinates information. We also propose a novel refine module
which shows the better performance compared with the general ones by using a
deconvolution layer instead of a bilinear upsampling layer. Our proposed method
shows the fastest speed among the existing methods with a competitive accuracy.
On DAVIS 2016 validation set, our method achieves 63.5 fps and J&F score of
81.6%.

Computer Vision and Pattern Recognition