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
@article{arxiv.2406.19665,
title = {PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation},
author = {Zhangjing Yang and Dun Liu and Xin Wang and Zhe Li and Barathwaj Anandan and Yi Wu},
journal= {arXiv preprint arXiv:2406.19665},
year = {2024}
}