A comprehensive exploration of Meta AI's Segment Anything Model (SAM), a foundation model designed to generalize across various segmentation tasks with minimal prompting, zero-shot and few-shot learning capabilities, and applications in a wide range of domains.

The paper introduces a novel architecture called residual networks (ResNets), which significantly improves deep neural network training by using skip connections to mitigate the vanishing gradient problem. This approach achieved state-of-the-art performance on several benchmarks, including the ImageNet dataset, and has become foundational in modern deep learning applications.