Learning What to Learn for Video Object Segmentation

• URL: http://arxiv.org/abs/2003.11540v2
• Date: Fri, 1 May 2020 16:10:19 GMT
• Title: Learning What to Learn for Video Object Segmentation
• Authors: Goutam Bhat, Felix J\"aremo Lawin, Martin Danelljan, Andreas Robinson, Michael Felsberg, Luc Van Gool, Radu Timofte
• Abstract summary: We introduce an end-to-end trainable VOS architecture that integrates a differentiable few-shot learning module. This internal learner is designed to predict a powerful parametric model of the target. We set a new state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an overall score of 81.5.
• Score: 157.4154825304324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video object segmentation (VOS) is a highly challenging problem, since the target object is only defined during inference with a given first-frame reference mask. The problem of how to capture and utilize this limited target information remains a fundamental research question. We address this by introducing an end-to-end trainable VOS architecture that integrates a differentiable few-shot learning module. This internal learner is designed to predict a powerful parametric model of the target by minimizing a segmentation error in the first frame. We further go beyond standard few-shot learning techniques by learning what the few-shot learner should learn. This allows us to achieve a rich internal representation of the target in the current frame, significantly increasing the segmentation accuracy of our approach. We perform extensive experiments on multiple benchmarks. Our approach sets a new state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an overall score of 81.5, corresponding to a 2.6% relative improvement over the previous best result.

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