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Comprehensive comparison of the latest advanced object tracking methods including ByteTrack, SAMBA-MOTR, CAMELTrack, Cutie, and DAM4SAM. Analysis covers tracking-by-detection vs detection-by-tracking paradigms, performance metrics, computational efficiency, and real-world applications in autonomous driving, surveillance, and video analytics.

Learn how to enhance computer vision model performance by integrating metadata through early, middle, and late fusion techniques. Discover practical YOLO11 implementation examples and achieve up to 20% accuracy improvements in challenging classification tasks.

Class imbalance represents a significant challenge in machine learning where the distribution of classes in a dataset is heavily skewed. When one class substantially outnumbers others, models tend to develop a bias toward the majority class, potentially compromising their performance on minority classes. In this article we explore upsampling as an effective strategy to address class imbalance problems.