Sign language recognition (SLR) is a weakly supervised task that annotates sign videos as
textual glosses. Recent studies show that insufficient training caused by the lack of large …

Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …

Abstract Combining Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs)
with Recurrent Neural Networks (RNNs) for spatiotemporal forecasting has yielded …

Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

The pancreas is a vital organ in digestive system which has significant health implications. It
is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the …

Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar
echoes sequences based on current observations which can serve both meteorological …

Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …

In recent years traditional numerical methods for accurate weather prediction have been
increasingly challenged by deep learning methods. Numerous historical datasets used for …

Deep learning approaches have been widely adopted for precipitation nowcasting in recent
years. Previous studies mainly focus on proposing new model architectures to improve pixel …

In this paper, we introduce PredBench, a benchmark tailored for the holistic evaluation of
spatio-temporal prediction networks. Despite significant progress in this field, there remains …