PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation
This provides a cost-effective method for video instance segmentation applications by eliminating the need for manual video annotations, though it is incremental as it builds on the PM-VIS algorithm.
The paper tackles the problem of video instance segmentation without costly video annotations by adapting the PM-VIS algorithm to use image datasets, pseudo masks, and semi-supervised optimization, achieving high performance with a cost-effective solution.
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