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.

本研究提出了一种端到端的深度神经网络，结合了 Mask-RCNN 实例分割网络和 Conv-GRU 视觉记忆模块，用于解决半监督视频对象分割任务，实验结果表明该方法在 DAVIS 数据集上取得了令人满意的结果。