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Video object segmentation targets at segmenting a specific object throughouta video sequence, given only an annotated first frame. Recent deep learningbased approaches find it effective by fine-tuning a general-purposesegmentation model on the annotated frame using hundreds of iterations ofgradient descent. Despite the high accuracy these methods achieve, thefine-tuning process is inefficient and fail to meet the requirements of realworld applications. We propose a novel approach that uses a single forward passto adapt the segmentation model to the appearance of a specific object.Specifically, a second meta neural network named modulator is learned tomanipulate the intermediate layers of the segmentation network given limitedvisual and spatial information of the target object. The experiments show thatour approach is 70times faster than fine-tuning approaches while achievingsimilar accuracy.

Efficient Video Object Segmentation via Network Modulation