Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos

• URL: http://arxiv.org/abs/2101.02196v1
• Date: Wed, 6 Jan 2021 18:56:24 GMT
• Title: Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
• Authors: Bin Zhao, Goutam Bhat, Martin Danelljan, Luc Van Gool, Radu Timofte
• Abstract summary: We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. We use our resulting accurate masks for weakly supervised training of video object segmentation (VOS) networks. The additional data provides substantially better generalization performance leading to state-of-the-art results in both the VOS and more challenging tracking domain.
• Score: 159.02703673838639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmenting objects in videos is a fundamental computer vision task. The current deep learning based paradigm offers a powerful, but data-hungry solution. However, current datasets are limited by the cost and human effort of annotating object masks in videos. This effectively limits the performance and generalization capabilities of existing video segmentation methods. To address this issue, we explore weaker form of bounding box annotations. We introduce a method for generating segmentation masks from per-frame bounding box annotations in videos. To this end, we propose a spatio-temporal aggregation module that effectively mines consistencies in the object and background appearance across multiple frames. We use our resulting accurate masks for weakly supervised training of video object segmentation (VOS) networks. We generate segmentation masks for large scale tracking datasets, using only their bounding box annotations. The additional data provides substantially better generalization performance leading to state-of-the-art results in both the VOS and more challenging tracking domain.

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