  Learning-based methods for visual segmentation have made progress on
particular types of segmentation tasks, but are limited by the necessary
supervision, the narrow definitions of fixed tasks, and the lack of control
during inference for correcting errors. To remedy the rigidity and annotation
burden of standard approaches, we address the problem of few-shot segmentation:
given few image and few pixel supervision, segment any images accordingly. We
propose guided networks, which extract a latent task representation from any
amount of supervision, and optimize our architecture end-to-end for fast,
accurate few-shot segmentation. Our method can switch tasks without further
optimization and quickly update when given more guidance. We report the first
results for segmentation from one pixel per concept and show real-time
interactive video segmentation. Our unified approach propagates pixel
annotations across space for interactive segmentation, across time for video
segmentation, and across scenes for semantic segmentation. Our guided segmentor
is state-of-the-art in accuracy for the amount of annotation and time. See
http://github.com/shelhamer/revolver for code, models, and more details.
