Multiple Detector

Multiple detector systems aim to improve performance and robustness by combining the outputs of several individual detectors, each potentially specializing in different aspects of a task. Current research focuses on optimizing the fusion of these outputs, exploring diverse architectures like CNNs and transformers, and applying this approach to various domains including object detection, adversarial example detection, and machine translation. The enhanced accuracy, reliability, and adaptability offered by multiple detector systems have significant implications for applications ranging from autonomous driving and medical image analysis to cybersecurity and natural language processing.