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Found 4 papers, 2 papers with code
Density Matters: Improved Core-set for Active Domain Adaptive Segmentation
Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation.
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
Bases have become an integral part of modern deep learning-based models for time series forecasting due to their ability to act as feature extractors or future references.
Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting
Accurate prediction of the future given the past based on time series data is of paramount importance, since it opens the door for decision making and risk management ahead of time.
Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
To this end, we present a new large-scale dataset with comprehensive annotations, named Human-in-Events or HiEve (Human-centric video analysis in complex Events), for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd & complex events.
