Temporally stable video segmentation without video annotations

• URL: http://arxiv.org/abs/2110.08893v1
• Date: Sun, 17 Oct 2021 18:59:11 GMT
• Title: Temporally stable video segmentation without video annotations
• Authors: Aharon Azulay, Tavi Halperin, Orestis Vantzos, Nadav Bornstein, Ofir Bibi
• Abstract summary: We introduce a method to adapt still image segmentation models to video in an unsupervised manner. We verify that the consistency measure is well correlated with human judgement via a user study. We observe improvements in the generated segmented videos with minimal loss of accuracy.
• Score: 6.184270985214255
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Temporally consistent dense video annotations are scarce and hard to collect. In contrast, image segmentation datasets (and pre-trained models) are ubiquitous, and easier to label for any novel task. In this paper, we introduce a method to adapt still image segmentation models to video in an unsupervised manner, by using an optical flow-based consistency measure. To ensure that the inferred segmented videos appear more stable in practice, we verify that the consistency measure is well correlated with human judgement via a user study. Training a new multi-input multi-output decoder using this measure as a loss, together with a technique for refining current image segmentation datasets and a temporal weighted-guided filter, we observe stability improvements in the generated segmented videos with minimal loss of accuracy.

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