Recognition Task

Visual recognition tasks, aiming to enable computers to "see" and understand images, are a central focus in computer vision research. Current efforts concentrate on improving model robustness to various challenges like occlusions, noise, and data imbalance, often leveraging architectures such as Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), and employing techniques like contrastive learning, mixup augmentation, and parameter-efficient fine-tuning. These advancements are crucial for enhancing the reliability and efficiency of applications ranging from autonomous driving and medical image analysis to more specialized tasks like ancient text recognition and art classification.