Detection Paradigm

Detection paradigms encompass a broad range of methods aiming to accurately identify objects or anomalies within data, whether images, text, or sensor readings. Current research emphasizes integrated detection-correction frameworks, leveraging large language models or multi-task learning to improve accuracy and efficiency, particularly in challenging conditions like adverse weather or complex backgrounds. These advancements are crucial for improving applications across diverse fields, including autonomous driving, remote sensing, and natural language processing, by enhancing the reliability and robustness of detection systems. The focus is shifting towards incorporating contextual information and handling uncertainty more effectively to improve the overall performance and trustworthiness of detection models.