Cross-corpus

Cross-corpus refers to the use of the relationships and complementarities between different datasets in the field of computer vision, employing cross-dataset learning methods to improve the model's generalization and robustness. Its goal is to reduce reliance on a single dataset and enhance the model's adaptability to unseen data. The application value of cross-corpus lies in effectively addressing issues of uneven data distribution and insufficient labeled data, thereby improving the model's performance and reliability in real-world scenarios.