False Positive

False positives, the erroneous identification of non-target events or objects as targets, represent a significant challenge across diverse fields, hindering the reliability and efficiency of various systems. Current research focuses on mitigating false positives through improved model architectures (e.g., convolutional neural networks, transformers) and algorithms incorporating techniques like uncertainty estimation, multi-instance learning, and attention mechanisms, often combined with post-processing steps for refinement. Addressing this issue is crucial for enhancing the accuracy and trustworthiness of applications ranging from medical diagnosis and autonomous driving to social media moderation and astronomical data analysis, ultimately improving decision-making and resource allocation. The development of new evaluation metrics beyond traditional measures like mAP is also an active area of investigation.