TubeFormer-DeepLab: Video Mask Transformer

• URL: http://arxiv.org/abs/2205.15361v1
• Date: Mon, 30 May 2022 18:10:33 GMT
• Title: TubeFormer-DeepLab: Video Mask Transformer
• Authors: Dahun Kim, Jun Xie, Huiyu Wang, Siyuan Qiao, Qihang Yu, Hong-Seok Kim, Hartwig Adam, In So Kweon and Liang-Chieh Chen
• Abstract summary: We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner. TubeFormer-DeepLab directly predicts video tubes with task-specific labels.
• Score: 98.47947102154217
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present TubeFormer-DeepLab, the first attempt to tackle multiple core video segmentation tasks in a unified manner. Different video segmentation tasks (e.g., video semantic/instance/panoptic segmentation) are usually considered as distinct problems. State-of-the-art models adopted in the separate communities have diverged, and radically different approaches dominate in each task. By contrast, we make a crucial observation that video segmentation tasks could be generally formulated as the problem of assigning different predicted labels to video tubes (where a tube is obtained by linking segmentation masks along the time axis) and the labels may encode different values depending on the target task. The observation motivates us to develop TubeFormer-DeepLab, a simple and effective video mask transformer model that is widely applicable to multiple video segmentation tasks. TubeFormer-DeepLab directly predicts video tubes with task-specific labels (either pure semantic categories, or both semantic categories and instance identities), which not only significantly simplifies video segmentation models, but also advances state-of-the-art results on multiple video segmentation benchmarks

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