CRVOS: Clue Refining Network for Video Object Segmentation

The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attentions 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 Clue Refining Network for Video Object Segmentation (CRVOS) which does not have complex intermediate network. 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 higher performance than general ones by using deconvolution layer instead of bilinear upsampling. Our proposed network, CRVOS, is the fastest method with the competitive performance. On DAVIS16 validation set, CRVOS achieves 61 FPS and J&F score of 81.6%.