Classification Pipeline

Image classification pipelines aim to efficiently and accurately categorize input data, with recent research focusing on improving robustness against adversarial attacks and handling imbalanced datasets (long-tail problems). This involves exploring diverse architectures, including Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), often incorporating preprocessing steps like segmentation and data augmentation techniques such as clustering to enhance performance. These advancements have significant implications for various applications, from medical image analysis and environmental monitoring to legal text processing and resource-constrained edge devices.