detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2410.03936
Tema
Computer Science - Computer Vision... Computer Science - Artificial Inte... Computer Science - Machine Learnin...Autor
Ghasemabadi, Amirhosein Janjua, Muhammad Kamran Salameh, Mohammad Niu, DiCategoría
Computer Science
Año
2024
fecha de cotización
23/10/2024
Palabras clave
turtle model science causal truncated video computerResumen
One key challenge to video restoration is to model the transition dynamics of video frames governed by motion.
In this work, we propose TURTLE to learn the truncated causal history model for efficient and high-performing video restoration.
Unlike traditional methods that process a range of contextual frames in parallel, TURTLE enhances efficiency by storing and summarizing a truncated history of the input frame latent representation into an evolving historical state.
This is achieved through a sophisticated similarity-based retrieval mechanism that implicitly accounts for inter-frame motion and alignment.
The causal design in TURTLE enables recurrence in inference through state-memorized historical features while allowing parallel training by sampling truncated video clips.
We report new state-of-the-art results on a multitude of video restoration benchmark tasks, including video desnowing, nighttime video deraining, video raindrops and rain streak removal, video super-resolution, real-world and synthetic video deblurring, and blind video denoising while reducing the computational cost compared to existing best contextual methods on all these tasks.
;Comment: Accepted to NeurIPS 2024.
24 pages
Ghasemabadi, Amirhosein,Janjua, Muhammad Kamran,Salameh, Mohammad,Niu, Di, 2024, Learning Truncated Causal History Model for Video Restoration
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