  An interactive video object segmentation algorithm, which takes scribble
annotations on query objects as input, is proposed in this paper. We develop a
deep neural network, which consists of the annotation network (A-Net) and the
transfer network (T-Net). First, given user scribbles on a frame, A-Net yields
a segmentation result based on the encoder-decoder architecture. Second, T-Net
transfers the segmentation result bidirectionally to the other frames, by
employing the global and local transfer modules. The global transfer module
conveys the segmentation information in an annotated frame to a target frame,
while the local transfer module propagates the segmentation information in a
temporally adjacent frame to the target frame. By applying A-Net and T-Net
alternately, a user can obtain desired segmentation results with minimal
efforts. We train the entire network in two stages, by emulating user scribbles
and employing an auxiliary loss. Experimental results demonstrate that the
proposed interactive video object segmentation algorithm outperforms the
state-of-the-art conventional algorithms. Codes and models are available at
https://github.com/yuk6heo/IVOS-ATNet.
