Box Supervised Video Segmentation Proposal Network

• URL: http://arxiv.org/abs/2202.07025v2
• Date: Wed, 16 Feb 2022 22:09:01 GMT
• Title: Box Supervised Video Segmentation Proposal Network
• Authors: Tanveer Hannan, Rajat Koner, Jonathan Kobold, Matthias Schubert
• Abstract summary: We propose a box-supervised video object segmentation proposal network, which takes advantage of intrinsic video properties. The proposed method outperforms the state-of-the-art self-supervised benchmark by 16.4% and 6.9%. We provide extensive tests and ablations on the datasets, demonstrating the robustness of our method.
• Score: 3.384080569028146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video Object Segmentation (VOS) has been targeted by various fully-supervised and self-supervised approaches. While fully-supervised methods demonstrate excellent results, self-supervised ones, which do not use pixel-level ground truth, attract much attention. However, self-supervised approaches pose a significant performance gap. Box-level annotations provide a balanced compromise between labeling effort and result quality for image segmentation but have not been exploited for the video domain. In this work, we propose a box-supervised video object segmentation proposal network, which takes advantage of intrinsic video properties. Our method incorporates object motion in the following way: first, motion is computed using a bidirectional temporal difference and a novel bounding box-guided motion compensation. Second, we introduce a novel motion-aware affinity loss that encourages the network to predict positive pixel pairs if they share similar motion and color. The proposed method outperforms the state-of-the-art self-supervised benchmark by 16.4% and 6.9% $\mathcal{J}$ &$\mathcal{F}$ score and the majority of fully supervised methods on the DAVIS and Youtube-VOS dataset without imposing network architectural specifications. We provide extensive tests and ablations on the datasets, demonstrating the robustness of our method.

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