PM-VIS+：无需视频标注的高性能视频实例分割
PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation
AI总结：
本文提出PM-VIS+方法，通过利用图像数据集、ImageNet-bbox补充类别、伪掩码与半监督优化，在无需视频标注的情况下实现高性能视频实例分割。
摘要：
视频实例分割要求在视频中检测、分割和跟踪目标，通常依赖昂贵的视频标注。本文提出了一种利用图像数据集来消除视频标注的方法。PM-VIS算法被改进为能够动态处理边界框和实例级像素标注。我们引入ImageNet-bbox来补充视频数据集中缺失的类别，并提出PM-VIS+算法以根据标注类型调整监督方式。为了提高精度，我们在未标注的视频数据上使用伪掩码和半监督优化技术。该方法在无需人工视频标注的情况下实现了较高的视频实例分割性能，为视频实例分割应用提供了一种经济高效的解决方案和新的视角。代码将发布在https://github.com/ldknight/PM-VIS-plus。
英文摘要：
Video instance segmentation requires detecting, segmenting, and tracking objects in videos, typically relying on costly video annotations. This paper introduces a method that eliminates video annotations by utilizing image datasets. The PM-VIS algorithm is adapted to handle both bounding box and instance-level pixel annotations dynamically. We introduce ImageNet-bbox to supplement missing categories in video datasets and propose the PM-VIS+ algorithm to adjust supervision based on annotation types. To enhance accuracy, we use pseudo masks and semi-supervised optimization techniques on unannotated video data. This method achieves high video instance segmentation performance without manual video annotations, offering a cost-effective solution and new perspectives for video instance segmentation applications. The code will be available in https://github.com/ldknight/PM-VIS-plus
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
