Patch Tensor

Patch tensors represent image data as multi-dimensional arrays, enabling efficient processing of spatial and spectral information. Current research focuses on leveraging patch tensors within various deep learning architectures, including transformers and convolutional neural networks, often employing tensor decompositions (like Tucker or Tensor Train) for dimensionality reduction and improved computational efficiency. This approach enhances performance in diverse applications such as image classification, trajectory prediction, and change detection in hyperspectral imagery, particularly for tasks involving small targets or complex scenes where capturing intricate relationships between data points is crucial. The resulting improvements in accuracy and computational efficiency are significant for advancing computer vision and related fields.