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Computer Science > Computer Vision and Pattern Recognition

arXiv:2212.02059 (cs)
[Submitted on 5 Dec 2022]

Title:Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition

Authors:Taehyeon Kim, Shinhwan Kang, Hyeonjeong Shin, Deukryeol Yoon, Seongha Eom, Kijung Shin, Se-Young Yun
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Abstract:The Weather4Cast competition (hosted by NeurIPS 2022) required competitors to predict super-resolution rain movies in various regions of Europe when low-resolution satellite contexts covering wider regions are given. In this paper, we show that a general baseline 3D U-Net can be significantly improved with region-conditioned layers as well as orthogonality regularizations on 1x1x1 convolutional layers. Additionally, we facilitate the generalization with a bag of training strategies: mixup data augmentation, self-distillation, and feature-wise linear modulation (FiLM). Presented modifications outperform the baseline algorithms (3D U-Net) by up to 19.54% with less than 1% additional parameters, which won the 4th place in the core test leaderboard.
Comments: workshop at NeurIPS 2022 Competition Track on Weather4Cast
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2212.02059 [cs.CV]
  (or arXiv:2212.02059v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2212.02059

Submission history

From: Taehyeon Kim [view email]
[v1] Mon, 5 Dec 2022 06:50:08 UTC (76 KB)
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