  This paper presents a method for automatic video object segmentation based on
the fusion of motion stream, appearance stream, and instance-aware
segmentation. The proposed scheme consists of a two-stream fusion network and
an instance segmentation network. The two-stream fusion network again consists
of motion and appearance stream networks, which extract long-term temporal and
spatial information, respectively. Unlike the existing two-stream fusion
methods, the proposed fusion network blends the two streams at the original
resolution for obtaining accurate segmentation boundary. We develop a recurrent
bidirectional multiscale structure with skip connection for the stream fusion
network to extract long-term temporal information. Also, the multiscale
structure enables to obtain the original resolution features at the end of the
network. As a result of two-stream fusion, we have a pixel-level probabilistic
segmentation map, which has higher values at the pixels belonging to the
foreground object. By combining the probability of foreground map and
objectness score of instance segmentation mask, we finally obtain foreground
segmentation results for video sequences without any user intervention, i.e.,
we achieve successful automatic video segmentation. The proposed structure
shows a state-of-the-art performance for automatic video object segmentation
task, and also achieves near semi-supervised performance.
