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Found 20 papers, 12 papers with code
Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
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.
Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting
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.
A Multi-In-Single-Out Network for Video Frame Interpolation without Optical Flow
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.
Prototype-oriented Unsupervised Change Detection for Disaster Management
Climate change has led to an increased frequency of natural disasters such as floods and cyclones.
Improved Flood Insights: Diffusion-Based SAR to EO Image Translation
Driven by rapid climate change, the frequency and intensity of flood events are increasing.
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation
Unlike existing semi-supervised semantic segmentation frameworks, CAFS constructs a validation set on a labeled dataset, to leverage the calibration performance for each class.
Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation
In specific, we achieve domain mixup in two-step: cut and paste.
Implicit Stacked Autoregressive Model for Video Prediction
Future frame prediction has been approached through two primary methods: autoregressive and non-autoregressive.
Ranked #1 on
Weather Forecasting
on SEVIR
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network
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.
Self-Pair: Synthesizing Changes from Single Source for Object Change Detection in Remote Sensing Imagery
For change detection in remote sensing, constructing a training dataset for deep learning models is difficult due to the requirements of bi-temporal supervision.
Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift
In this paper, we propose a training strategy to make the weather prediction model robust to spatial-temporal shifts.
Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network
Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems.
1st Place Solution to NeurIPS 2022 Challenge on Visual Domain Adaptation
The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised domain adaptive model in semantic segmentation tasks for industrial waste sorting.
Bag of Tricks for Domain Adaptive Multi-Object Tracking
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.
Source Domain Subset Sampling for Semi-Supervised Domain Adaptation in Semantic Segmentation
In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation.
Unsupervised Change Detection Based on Image Reconstruction Loss
In this paper, we propose unsupervised change detection based on image reconstruction loss using only unlabeled single temporal single image.
PT4AL: Using Self-Supervised Pretext Tasks for Active Learning
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)
Exploiting Features with Split-and-Share Module
Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks.
Sequential Feature Filtering Classifier
We propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for convolutional neural networks (CNNs).
