Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Understand

Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-shot transfer performance and high versatility for numerous vision applications (like image editing with fine-grained control).

• Many of such applications need to be run on resource-constraint edge devices, like mobile phones.
• In this work, we aim to make SAM mobile-friendly by replacing the heavyweight image encoder with a lightweight one.
• A naive way to train such a new SAM as in the original SAM paper leads to unsatisfactory performance, especially when limited training sources are available.