Binary Segmentation

Binary segmentation, a fundamental computer vision task, aims to classify each pixel in an image into one of two categories (e.g., foreground/background, object/non-object). Current research emphasizes improving accuracy and robustness across diverse applications, focusing on deep learning architectures like U-Net and transformers, often incorporating techniques such as generative adversarial networks (GANs) to handle imbalanced datasets and test-time training to adapt to unseen anomalies. These advancements are crucial for various fields, including medical image analysis (e.g., polyp, lesion, and neuron segmentation), industrial quality control (anomaly detection), and remote sensing (land cover classification), where accurate and efficient binary segmentation is essential for diagnosis, automation, and monitoring.