V astTrack consists of a few attractive properties: (1) V ast Object Category . In particular, it covers targets from 2,115 categories, significantly surpassing object classes of existing popular benchmarks ( e.g ., GOT -10k with 563 classes and LaSOT with 70 categories). Through providing such vast object classes, we expect to learn more general object tracking.

The application of low-order decomposition results in considerable memory savings while preserving the features essential for learning, and also offers theoretical guarantees to convergence.

These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial.

The objective of dataset condensation is to ensure that the model trained with the synthetic dataset can perform comparably to the model trained with full datasets. However, existing methods predominantly concentrate on classification tasks, posing challenges in their adaptation to time series forecasting (TS-forecasting).