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arXiv · 2406.19665

PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation

Zhangjing Yang·Dun Liu·Xin Wang·Zhe Li·Barathwaj Anandan·Yi Wu
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Abstract

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

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Zhangjing Yang, Dun Liu, Xin Wang, Zhe Li, Barathwaj Anandan, Yi Wu. 2024-06-28. PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation. https://arxiv.org/abs/2406.19665

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