Patch Embeddings

Patch embeddings, representing image or signal segments as vectors, are a core component of many modern computer vision and signal processing methods, particularly those using Vision Transformers (ViTs). Current research focuses on improving patch embedding generation and utilization for tasks like semantic segmentation, denoising, and few-shot learning, often incorporating techniques like contrastive learning and multi-scale approaches to enhance feature representation and model robustness. These advancements are significantly impacting various fields, improving the efficiency and accuracy of image analysis in applications ranging from medical image analysis to forensic science and anomaly detection.