  We provide a high fidelity deep learning algorithm (HyperSeg) for interactive
video segmentation tasks using a convolutional network with context-aware skip
connections, and compressed, hypercolumn image features combined with a
convolutional tessellation procedure. In order to maintain high output
fidelity, our model crucially processes and renders all image features in high
resolution, without utilizing downsampling or pooling procedures. We maintain
this consistent, high grade fidelity efficiently in our model chiefly through
two means: (1) We use a statistically-principled tensor decomposition procedure
to modulate the number of hypercolumn features and (2) We render these features
in their native resolution using a convolutional tessellation technique. For
improved pixel level segmentation results, we introduce a boundary loss
function; for improved temporal coherence in video data, we include temporal
image information in our model. Through experiments, we demonstrate the
improved accuracy of our model against baseline models for interactive
segmentation tasks using high resolution video data. We also introduce a
benchmark video segmentation dataset, the VFX Segmentation Dataset, which
contains over 27,046 high resolution video frames, including greenscreen and
various composited scenes with corresponding, hand crafted, pixel level
segmentations. Our work presents an extension to improvement to state of the
art segmentation fidelity with high resolution data and can be used across a
broad range of application domains, including VFX pipelines and medical imaging
disciplines.
