A review on deep learning techniques for video prediction

S Oprea, P Martinez-Gonzalez… - … on Pattern Analysis …, 2020 - ieeexplore.ieee.org
The ability to predict, anticipate and reason about future outcomes is a key component of
intelligent decision-making systems. In light of the success of deep learning in computer …

Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while slee**. This reduction in …

Gaia-1: A generative world model for autonomous driving

A Hu, L Russell, H Yeo, Z Murez, G Fedoseev… - arxiv preprint arxiv …, 2023 - arxiv.org
Autonomous driving promises transformative improvements to transportation, but building
systems capable of safely navigating the unstructured complexity of real-world scenarios …

Phenaki: Variable length video generation from open domain textual description

R Villegas, M Babaeizadeh, PJ Kindermans… - arxiv preprint arxiv …, 2022 - arxiv.org
We present Phenaki, a model capable of realistic video synthesis, given a sequence of
textual prompts. Generating videos from text is particularly challenging due to the …

Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving

Y Wang, J He, L Fan, H Li, Y Chen… - Proceedings of the …, 2024 - openaccess.thecvf.com
In autonomous driving predicting future events in advance and evaluating the foreseeable
risks empowers autonomous vehicles to plan their actions enhancing safety and efficiency …

Mcvd-masked conditional video diffusion for prediction, generation, and interpolation

V Voleti, A Jolicoeur-Martineau… - Advances in neural …, 2022 - proceedings.neurips.cc
Video prediction is a challenging task. The quality of video frames from current state-of-the-
art (SOTA) generative models tends to be poor and generalization beyond the training data …

Zero-shot robotic manipulation with pretrained image-editing diffusion models

K Black, M Nakamoto, P Atreya, H Walke… - arxiv preprint arxiv …, 2023 - arxiv.org
If generalist robots are to operate in truly unstructured environments, they need to be able to
recognize and reason about novel objects and scenarios. Such objects and scenarios might …

[HTML][HTML] Diffusion probabilistic modeling for video generation

R Yang, P Srivastava, S Mandt - Entropy, 2023 - mdpi.com
Denoising diffusion probabilistic models are a promising new class of generative models
that mark a milestone in high-quality image generation. This paper showcases their ability to …

Predrnn: A recurrent neural network for spatiotemporal predictive learning

Y Wang, H Wu, J Zhang, Z Gao, J Wang… - … on Pattern Analysis …, 2022 - ieeexplore.ieee.org
The predictive learning of spatiotemporal sequences aims to generate future images by
learning from the historical context, where the visual dynamics are believed to have modular …

Temporal attention unit: Towards efficient spatiotemporal predictive learning

C Tan, Z Gao, L Wu, Y Xu, J **a… - Proceedings of the …, 2023 - openaccess.thecvf.com
Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …