Label Assignment Strategy

Label assignment strategies in object detection and related tasks aim to optimize the mapping of ground truth labels to model predictions during training, significantly impacting model performance. Current research focuses on improving these strategies for various architectures, including transformer-based models and anchor-based detectors, by addressing issues like imbalanced training data, inefficient use of positive samples, and the need for adaptive thresholds. These advancements lead to more accurate and efficient object detection, visual relationship detection, and multi-object tracking, with implications for applications ranging from autonomous driving to medical image analysis.