Video object segmentation, aiming to segment the foreground objects given the
annotation of the first frame, has been attracting increasing attentions. Many
state-of-the-art approaches have achieved great performance by relying on
online model updating or mask-propagation techniques. However, most online
models require high computational cost due to model fine-tuning during
inference. Most mask-propagation based models are faster but with relatively
low performance due to failure to adapt to object appearance variation. In this
paper, we are aiming to design a new model to make a good balance between speed
and performance. We propose a model, called NPMCA-net, which directly localizes
foreground objects based on mask-propagation and non-local technique by
matching pixels in reference and target frames. Since we bring in information
of both first and previous frames, our network is robust to large object
appearance variation, and can better adapt to occlusions. Extensive experiments
show that our approach can achieve a new state-of-the-art performance with a
fast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on
DAVIS-2017, with speed of 0.11s per frame) under the same level comparison.
Source code is available at this https URL

本文提出了一种名为 NPMCA-net 的模型，它结合了 mask-propagation 和非局部技术，直接定位前景物体，既能处理大的物体外观变化，也能更好地适应遮挡，实验结果表明，该方法在速度和性能方面都达到了新的最高水平。