تقرير
PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation
| العنوان: | PM-VIS+: High-Performance Video Instance Segmentation without Video Annotation |
|---|---|
| المؤلفون: | Yang, Zhangjing, Liu, Dun, Wang, Xin, Li, Zhe, Anandan, Barathwaj, Wu, Yi |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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 Comment: MIPR 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.19665 |
| رقم الأكسشن: | edsarx.2406.19665 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |