  In recent years, the task of segmenting foreground objects from background in
a video, i.e. video object segmentation (VOS), has received considerable
attention. In this paper, we propose a single end-to-end trainable deep neural
network, convolutional gated recurrent Mask-RCNN, for tackling the
semi-supervised VOS task. We take advantage of both the instance segmentation
network (Mask-RCNN) and the visual memory module (Conv-GRU) to tackle the VOS
task. The instance segmentation network predicts masks for instances, while the
visual memory module learns to selectively propagate information for multiple
instances simultaneously, which handles the appearance change, the variation of
scale and pose and the occlusions between objects. After offline and online
training under purely instance segmentation losses, our approach is able to
achieve satisfactory results without any post-processing or synthetic video
data augmentation. Experimental results on DAVIS 2016 dataset and DAVIS 2017
dataset have demonstrated the effectiveness of our method for video object
segmentation task.
