no code implementations • 7 Jun 2024 • Doyi Kim, Minseok Seo, Yeji Choi
Recently, data-driven weather forecasting methods have received significant attention for surpassing the RMSE performance of traditional NWP (Numerical Weather Prediction)-based methods.
no code implementations • 28 Jan 2024 • Young-Jae Park, Minseok Seo, Doyi Kim, Hyeri Kim, Sanghoon Choi, Beomkyu Choi, Jeongwon Ryu, Sohee Son, Hae-Gon Jeon, Yeji Choi
Our model provides predictions at 6-hour intervals for up to 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models.
1 code implementation • 5 Dec 2023 • Donggeun Yoon, Minseok Seo, Doyi Kim, Yeji Choi, Donghyeon Cho
We also introduce and evaluate the Pacific Northwest Windstorm (PNW)-Typhoon weather satellite dataset to verify the effectiveness of DGDM in high-resolution regional forecasting.
1 code implementation • 20 Nov 2023 • Jaemin Lee, Minseok Seo, SangWoo Lee, Hyobin Park, Dong-Geol Choi
In general, deep learning-based video frame interpolation (VFI) methods have predominantly focused on estimating motion vectors between two input frames and warping them to the target time.
no code implementations • 15 Oct 2023 • Youngtack Oh, Minseok Seo, Doyi Kim, Junghoon Seo
Climate change has led to an increased frequency of natural disasters such as floods and cyclones.
no code implementations • 14 Jul 2023 • Minseok Seo, Youngtack Oh, Doyi Kim, Dongmin Kang, Yeji Choi
Driven by rapid climate change, the frequency and intensity of flood events are increasing.
1 code implementation • 26 Mar 2023 • Dina Bashkirova, Samarth Mishra, Diala Lteif, Piotr Teterwak, Donghyun Kim, Fadi Alladkani, James Akl, Berk Calli, Sarah Adel Bargal, Kate Saenko, Daehan Kim, Minseok Seo, YoungJin Jeon, Dong-Geol Choi, Shahaf Ettedgui, Raja Giryes, Shady Abu-Hussein, Binhui Xie, Shuang Li
To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
1 code implementation • 21 Mar 2023 • Jingi Ju, Hyeoncheol Noh, Yooseung Wang, Minseok Seo, Dong-Geol Choi
Unlike existing semi-supervised semantic segmentation frameworks, CAFS constructs a validation set on a labeled dataset, to leverage the calibration performance for each class.
no code implementations • 17 Mar 2023 • Daehan Kim, Minseok Seo, KwanYong Park, Inkyu Shin, Sanghyun Woo, In-So Kweon, Dong-Geol Choi
In specific, we achieve domain mixup in two-step: cut and paste.
1 code implementation • 14 Mar 2023 • Minseok Seo, Hakjin Lee, Doyi Kim, Junghoon Seo
Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive.
Ranked #1 on Video Prediction on Human3.6M
no code implementations • 8 Mar 2023 • Minseok Seo, Yeji Choi, Hyungon Ry, Heesun Park, Hyungkun Bae, Hyesook Lee, Wanseok Seo
Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions.
1 code implementation • 20 Dec 2022 • Minseok Seo, Hakjin Lee, Yongjin Jeon, Junghoon Seo
For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision.
1 code implementation • 6 Dec 2022 • Minseok Seo, Doyi Kim, Seungheon Shin, Eunbin Kim, Sewoong Ahn, Yeji Choi
In this paper, we propose a training strategy to make the weather prediction model robust to spatial-temporal shifts.
1 code implementation • 6 Dec 2022 • Minseok Seo, Doyi Kim, Seungheon Shin, Eunbin Kim, Sewoong Ahn, Yeji Choi
Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems.
1 code implementation • 26 Nov 2022 • Daehan Kim, Minseok Seo, YoungJin Jeon, Dong-Geol Choi
The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised domain adaptive model in semantic segmentation tasks for industrial waste sorting.
1 code implementation • 31 May 2022 • Minseok Seo, Jeongwon Ryu, Kwangjin Yoon
To maximize the performance on real data, we first propose to use pseudo-labeling that generates imperfect labels for real data using a model trained with synthetic dataset.
no code implementations • 30 Apr 2022 • Daehan Kim, Minseok Seo, Jinsun Park, Dong-Geol Choi
In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation.
1 code implementation • 4 Apr 2022 • Hyeoncheol Noh, Jingi Ju, Minseok Seo, Jongchan Park, Dong-Geol Choi
In this paper, we propose unsupervised change detection based on image reconstruction loss using only unlabeled single temporal single image.
1 code implementation • 19 Jan 2022 • John Seon Keun Yi, Minseok Seo, Jongchan Park, Dong-Geol Choi
Before the active learning iterations, the pretext task learner is trained on the unlabeled set, and the unlabeled data are sorted and split into batches by their pretext task losses.
Ranked #2 on Active Learning on CIFAR10 (10,000)
no code implementations • 10 Aug 2021 • Jaemin Lee, Minseok Seo, Jongchan Park, Dong-Geol Choi
Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks.
no code implementations • 21 Jun 2020 • Minseok Seo, Jaemin Lee, Jongchan Park, Dong-Geol Choi
We propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for convolutional neural networks (CNNs).