Meta AI’s Segment Anything Model (SAM) is a groundbreaking tool in computer vision, designed to perform image segmentation tasks with remarkable precision and versatility. Most notably it is capable of zero-shot generalization, identifying unfamiliar objects and images without the need for additional training

Building upon SAM, Meta AI introduced SAM2, extending segmentation capabilities to video data.

We compare results for the SAM-vit-base and SAM2-large models.

Both models show a similar pattern, with the first layers (near the data) having layer alphas in the red-zone, indicating they may be overfit. Then, later, the layers closer to the labels have alphas in the HTSR safe-zone, between 2 and 6. This is quite unique, and suggests an interesting mechanism for zero-shot learning.

This structure helps with zero-shot learning by separating general and specific information. The layers near the data memorize high-level, general features that can be reused for different tasks. The layers closer to the labels take this general information and turn it into task-specific outputs. By organizing information in this way, the models can handle new tasks they haven’t been trained on without needing additional fine-tuning.
This ability to reuse general features makes the models good at solving problems they haven’t seen before, enabling strong zero-shot performance!