11 accepted papers
We present Upsample Anything, a lightweight test-time optimization (TTO) framework that restores low-resolution features to high-resolution, pixel-wise outputs without any training. Although Vision Foundation Models demonstrate strong generalization across diverse downstream tasks, their representat
Precipitation forecasting relies on heterogeneous data sets. Weather radar is accurate, but coverage is geographically limited and costly to maintain. Weather stations provide accurate but sparse point measurements, while satellites offer dense, high-resolution coverage without direct rainfall retr…
Accurate precipitation forecasting is crucial for early warnings of disasters, such as floods and landslides. Traditional forecasts rely on ground-based radar systems, which are space-constrained and have high maintenance costs. Consequently, most developing countries depend on a global numerical mo…
Accurate video forecasting enables autonomous vehicles to anticipate hazards, robotics and surveillance systems to predict human intent, and environmental models to issue timely warnings for extreme weather events. However, existing methods remain limited: transformers rely on global attention with…
In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction is crucial for effective damage control. Traditional physics-based models, while comprehensive, are computationally intensive and rely heavily on the expertise of forec…
"Weather forecasting requires both deterministic outcomes for immediate decision-making and probabilistic results for assessing uncertainties. However, deterministic models may not fully capture the spectrum of weather possibilities, and probabilistic forecasting can lack the precision needed for sp…
Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied to the domain adaptation task, where we can mix the source and target samples to obtain domain-mixed samples for better adaptation. Ho…
Multi-camera instance association, which identifies identical objects among multiple objects in multi-view images, is challenging due to several harsh constraints. To tackle this problem, most studies have employed CNNs as feature extractors but often fail under such harsh constraints. Inspired by V…
The common paradigm of CNN-based action recognition modelsis to simply use the average of the dense predictions from every frame. However, these dense predictions are inefficient since all frames are evenly utilized regardless of the existence of the action. In real-time action recognition applicati
"Labeling a large set of data is expensive. Active learning aims to tackle this problem by asking to annotate only the most informative data from the unlabeled set. We propose a novel active learning approach that utilizes self-supervised pretext tasks and a unique data sampler to select data that a…
Accurate and fast inventory management algorithms are essential in the modern distribution industry. However, the configuration process of inventory management algorithms is very expensive, and the direct comprehensive management of inventory procedures is labor intensive and inaccurate. Therefore,…