MFC-RFNet integrates multi-scale bidirectional communication, condition-guided alignment, and rectified flow to produce clearer and more skillful radar precipitation forecasts than prior baselines on four public datasets.
Improving the training of rectified flows
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
Step-Video-T2V describes a 30B-parameter text-to-video model with custom Video-VAE, 3D DiT, flow matching, and Video-DPO that claims state-of-the-art results on a new internal benchmark.
citing papers explorer
-
MFC-RFNet: A Multi-scale Guided Rectified Flow Network for Radar Sequence Prediction
MFC-RFNet integrates multi-scale bidirectional communication, condition-guided alignment, and rectified flow to produce clearer and more skillful radar precipitation forecasts than prior baselines on four public datasets.
-
Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model
Step-Video-T2V describes a 30B-parameter text-to-video model with custom Video-VAE, 3D DiT, flow matching, and Video-DPO that claims state-of-the-art results on a new internal benchmark.