Detection Model

Detection models, encompassing a broad range of algorithms and architectures like convolutional neural networks (CNNs), transformers, and generative adversarial networks (GANs), aim to accurately identify and classify objects or events within various data types, including images, videos, and time series. Current research emphasizes improving model robustness against noisy data, distribution shifts, and adversarial attacks, as well as enhancing interpretability and efficiency, particularly for resource-constrained environments. These advancements have significant implications across diverse fields, from improving security systems and medical diagnostics to optimizing industrial processes and enabling autonomous systems.