General Image Segmentation

General image segmentation aims to partition an image into meaningful regions, a crucial task in diverse fields like autonomous driving and medical imaging. Current research emphasizes developing robust and efficient segmentation models, focusing on architectures like transformers and convolutional neural networks, often combined in hybrid approaches, and exploring techniques for adapting pre-trained models (e.g., Segment Anything Model) to specific domains with minimal additional training. These advancements are driving improvements in accuracy, speed, and adaptability across various applications, impacting fields ranging from medical diagnosis to robotics.