  We developed a real-time, high-quality semi-supervised video object
segmentation algorithm. Its accuracy is on par with the most accurate,
time-consuming online-learning model, while its speed is similar to the fastest
template-matching method with sub-optimal accuracy. The core component of the
model is a novel global context module that effectively summarizes and
propagates information through the entire video. Compared to previous
approaches that only use one frame or a few frames to guide the segmentation of
the current frame, the global context module uses all past frames. Unlike the
previous state-of-the-art space-time memory network that caches a memory at
each spatio-temporal position, the global context module uses a fixed-size
feature representation. Therefore, it uses constant memory regardless of the
video length and costs substantially less memory and computation. With the
novel module, our model achieves top performance on standard benchmarks at a
real-time speed.
