High Resolution
High-resolution imaging and data processing are crucial for advancing numerous scientific fields, aiming to improve accuracy and detail in various applications. Current research focuses on developing and applying deep learning models, including diffusion models, transformers, and graph neural networks, to enhance resolution in diverse data types such as images, videos, and sensor readings. This work is significantly impacting fields ranging from weather forecasting and medical imaging to remote sensing and autonomous driving, enabling more precise analyses and improved decision-making. The development of high-resolution datasets and benchmark evaluations is also a key focus, facilitating the comparison and improvement of these advanced models.
Papers
On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models
Tariq Berrada Ifriqi, Pietro Astolfi, Melissa Hall, Reyhane Askari-Hemmat, Yohann Benchetrit, Marton Havasi, Matthew Muckley, Karteek Alahari, Adriana Romero-Soriano, Jakob Verbeek, Michal Drozdzal
Safety Verification for Evasive Collision Avoidance in Autonomous Vehicles with Enhanced Resolutions
Aliasghar Arab, Milad Khaleghi, Alireza Partovi, Alireza Abbaspour, Chaitanya Shinde, Yashar Mousavi, Vahid Azimi, Ali Karimmoddini
IceDiff: High Resolution and High-Quality Sea Ice Forecasting with Generative Diffusion Prior
Jingyi Xu, Siwei Tu, Weidong Yang, Shuhao Li, Keyi Liu, Yeqi Luo, Lipeng Ma, Ben Fei, Lei Bai
Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation
Jiahao Cui, Hui Li, Yao Yao, Hao Zhu, Hanlin Shang, Kaihui Cheng, Hang Zhou, Siyu Zhu, Jingdong Wang
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
Qian Liu, Bing Gong, Xiaoran Zhuang, Xiaohui Zhong, Zhiming Kang, Hao Li