Sign Classification

Sign classification research focuses on accurately identifying different signs, primarily in applications like autonomous driving and sign language recognition. Current efforts concentrate on improving the robustness and efficiency of classification models, employing architectures such as convolutional neural networks (CNNs) and vision transformers, often enhanced with techniques like adversarial training and data augmentation to mitigate vulnerabilities to adversarial attacks and improve generalization. These advancements are crucial for enhancing the safety and reliability of autonomous systems and improving accessibility for individuals using sign language, impacting both technological development and societal inclusion.