Peter Grönquist
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View article: Unlocking Comics: The AI4VA Dataset for Visual Understanding
Unlocking Comics: The AI4VA Dataset for Visual Understanding Open
In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital humanities, there is a growing demand to leverage technolo…
View article: OSPC: Artificial VLM Features for Hateful Meme Detection
OSPC: Artificial VLM Features for Hateful Meme Detection Open
The digital revolution and the advent of the world wide web have transformed\nhuman communication, notably through the emergence of memes. While memes are a\npopular and straightforward form of expression, they can also be used to spread\n…
View article: Efficient Temporally-aware DeepFake Detection using H.264 Motion Vectors
Efficient Temporally-aware DeepFake Detection using H.264 Motion Vectors Open
Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person’s expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements be…
View article: Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors Open
Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person's expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements be…
View article: Deep learning for post-processing ensemble weather forecasts
Deep learning for post-processing ensemble weather forecasts Open
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or …
View article: Deep Learning for Post-Processing Ensemble Weather Forecasts
Deep Learning for Post-Processing Ensemble Weather Forecasts Open
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many pe...
View article: Predicting Weather Uncertainty with Deep Convnets
Predicting Weather Uncertainty with Deep Convnets Open
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations. To provide accurate estimation, dozens of such computation…