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
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
Calum Green, Sharif Ahmed, Shashidhara Marathe, Liam Perera, Alberto Leonardi, Killian Gmyrek, Daniele Dini, James Le Houx
FuXi-2.0: Advancing machine learning weather forecasting model for practical applications
Xiaohui Zhong, Lei Chen, Xu Fan, Wenxu Qian, Jun Liu, Hao Li
AnomalyCD: A benchmark for Earth anomaly change detection with high-resolution and time-series observations
Jingtao Li, Qian Zhu, Xinyu Wang, Hengwei Zhao, Yanfei Zhong
DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generation
Wei Wu, Xi Guo, Weixuan Tang, Tingxuan Huang, Chiyu Wang, Dongyue Chen, Chenjing Ding
Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height and Cover from High-Resolution, Multi-Sensor Satellite Imagery
Manuel Weber, Carly Beneke, Clyde Wheeler
HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models in Resource-Constrained Environments
Kazi Hasan Ibn Arif, JinYi Yoon, Dimitrios S. Nikolopoulos, Hans Vandierendonck, Deepu John, Bo Ji