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We provide a high fidelity deep learning algorithm (HyperSeg) for interactivevideo segmentation tasks using a convolutional network with context-aware skipconnections, and compressed, hypercolumn image features combined with aconvolutional tessellation procedure. In order to maintain high outputfidelity, our model crucially processes and renders all image features in highresolution, without utilizing downsampling or pooling procedures. We maintainthis consistent, high grade fidelity efficiently in our model chiefly throughtwo means: (1) We use a statistically-principled tensor decomposition procedureto modulate the number of hypercolumn features and (2) We render these featuresin their native resolution using a convolutional tessellation technique. Forimproved pixel level segmentation results, we introduce a boundary lossfunction; for improved temporal coherence in video data, we include temporalimage information in our model. Through experiments, we demonstrate theimproved accuracy of our model against baseline models for interactivesegmentation tasks using high resolution video data. We also introduce abenchmark video segmentation dataset, the VFX Segmentation Dataset, whichcontains over 27,046 high resolution video frames, including greenscreen andvarious composited scenes with corresponding, hand crafted, pixel levelsegmentations. Our work presents an extension to improvement to state of theart segmentation fidelity with high resolution data and can be used across abroad range of application domains, including VFX pipelines and medical imagingdisciplines.

High Fidelity Interactive Video Segmentation Using Tensor Decomposition Boundary Loss Convolutional Tessellations and Context Aware Skip Connections