تقرير
Harnessing Meta-Learning for Improving Full-Frame Video Stabilization
| العنوان: | Harnessing Meta-Learning for Improving Full-Frame Video Stabilization |
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| المؤلفون: | Ali, Muhammad Kashif, Im, Eun Woo, Kim, Dongjin, Kim, Tae Hyun |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Video stabilization is a longstanding computer vision problem, particularly pixel-level synthesis solutions for video stabilization which synthesize full frames add to the complexity of this task. These techniques aim to stabilize videos by synthesizing full frames while enhancing the stability of the considered video. This intensifies the complexity of the task due to the distinct mix of unique motion profiles and visual content present in each video sequence, making robust generalization with fixed parameters difficult. In our study, we introduce a novel approach to enhance the performance of pixel-level synthesis solutions for video stabilization by adapting these models to individual input video sequences. The proposed adaptation exploits low-level visual cues accessible during test-time to improve both the stability and quality of resulting videos. We highlight the efficacy of our methodology of "test-time adaptation" through simple fine-tuning of one of these models, followed by significant stability gain via the integration of meta-learning techniques. Notably, significant improvement is achieved with only a single adaptation step. The versatility of the proposed algorithm is demonstrated by consistently improving the performance of various pixel-level synthesis models for video stabilization in real-world scenarios. Comment: CVPR 2024, Code will be made availble on: http://github.com/MKashifAli/MetaVideoStab |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.03662 |
| رقم الأكسشن: | edsarx.2403.03662 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |