  Learning a data-driven spatio-temporal semantic representation of the objects
is the key to coherent and consistent labelling in video. This paper proposes
to achieve semantic video object segmentation by learning a data-driven
representation which captures the synergy of multiple instances from continuous
frames. To prune the noisy detections, we exploit the rich information among
multiple instances and select the discriminative and representative subset.
This selection process is formulated as a facility location problem solved by
maximising a submodular function. Our method retrieves the longer term
contextual dependencies which underpins a robust semantic video object
segmentation algorithm. We present extensive experiments on a challenging
dataset that demonstrate the superior performance of our approach compared with
the state-of-the-art methods.
