  Personal robots and driverless cars need to be able to operate in novel
environments and thus quickly and efficiently learn to recognise new object
classes. We address this problem by considering the task of video object
segmentation. Previous accurate methods for this task finetune a model using
the first annotated frame, and/or use additional inputs such as optical flow
and complex post-processing. In contrast, we develop a fast, causal algorithm
that requires no finetuning, auxiliary inputs or post-processing, and segments
a variable number of objects in a single forward-pass. We represent an object
with clusters, or "visual words", in the embedding space, which correspond to
object parts in the image space. This allows us to robustly match to the
reference objects throughout the video, because although the global appearance
of an object changes as it undergoes occlusions and deformations, the
appearance of more local parts may stay consistent. We learn these visual words
in an unsupervised manner, using meta-learning to ensure that our training
objective matches our inference procedure. We achieve comparable accuracy to
finetuning based methods (whilst being 1 to 2 orders of magnitude faster), and
state-of-the-art in terms of speed/accuracy trade-offs on four video
segmentation datasets. Code is available at
https://github.com/harkiratbehl/MetaVOS.
