谷歌团队提出Video-kMaX: 一种简单的统一方法，用于在线和近在线视频全景分割

【推荐理由】文章提出了一种统一的方法，用于在线和近在线视频全景分割。该方法在视频语义分割方面取得了新的最先进水平。

Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation
Inkyu Shin, Dahun Kim, Qihang Yu, Jun Xie, Hong-Seok Kim, Bradley Green, In So Kweon, Kuk-Jin Yoon, Liang-Chieh Chen

[Google Research & KAIST]

【论文链接】https://arxiv.org/pdf/2304.04694.pdf

【摘要】视频全景分割（VPS）旨在通过分割所有像素并将对象与视频中的像素关联来实现全面的像素级场景理解。目前的解决方案可分为在线和几乎在线方法。随着时间的推移，每个类别都有其自己的专业设计，使得在不同类别之间适应模型变得非常困难。为了缓解这种差异，本文提出了一种统一的在线和几乎在线VPS方法。所提出的Video-kMaX的元体系结构由两个组件组成：片段内分割器（用于片段级分割）和跨片段关联器（用于超出片段的关联）。本文提出了片段-kMaX（片段k均值掩码变换器）和HiLA-MB（分层位置感知内存缓冲区）来实例化分割器和关联器。一般公式将在线场景作为特殊情况采用片段长度为一。在不需要花哨的东西的情况下，Video-kMaX在KITTI-STEP和VIPSeg的视频全景分割以及VSPW的视频语义分割方面取得了新的最先进水平。

谷歌团队提出Video-kMaX: 一种简单的统一方法，用于在线和近在线视频全景分割

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