Repetition Detection

Repetition detection encompasses identifying recurring patterns or sequences within various data types, aiming for accurate and efficient classification of repetitive elements from non-repetitive ones. Current research focuses on developing robust algorithms, including transformer-based models and graph neural networks, to address this challenge across diverse domains such as speech analysis, video processing, and genomic sequencing. These advancements are improving the accuracy and speed of repetition detection, with significant implications for applications ranging from clinical assessment and action recognition to metagenomic analysis and image matching.