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Semi-supervised video object segmentation has made significant progress onreal and challenging videos in recent years. The current paradigm forsegmentation methods and benchmark datasets is to segment objects in videoprovided a single annotation in the first frame. However, we find thatsegmentation performance across the entire video varies dramatically whenselecting an alternative frame for annotation. This paper address the problemof learning to suggest the single best frame across the video for userannotation-this is, in fact, never the first frame of video. We achieve this byintroducing BubbleNets, a novel deep sorting network that learns to selectframes using a performance-based loss function that enables the conversion ofexpansive amounts of training examples from already existing datasets. UsingBubbleNets, we are able to achieve an 11% relative improvement in segmentationperformance on the DAVIS benchmark without any changes to the underlying methodof segmentation.

BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames