Patch Correlation

Patch correlation research focuses on leveraging the relationships between image or data patches to improve various computer vision and machine learning tasks. Current efforts concentrate on developing methods that explicitly model these correlations, often employing transformer architectures or other neural networks to capture both local and global relationships within and across different data modalities (e.g., images, 3D shapes). This work is significant because effectively utilizing patch correlations leads to improved performance in diverse applications, including image super-resolution, semantic segmentation, anomaly detection, and multi-view 3D understanding, often surpassing traditional methods.