@inproceedings{
mengzhen2024segment,
title={Segment Anything with Precise Interaction},
author={Mengzhen Liu and Mengyu Wang and Henghui Ding and Yilong Xu and Yao Zhao and Yunchao Wei},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=lD9A7SS4BP}
}
Although the Segment Anything Model (SAM) has achieved impressive results in many segmentation tasks and benchmarks, its performance noticeably deteriorates when applied to high-resolution images for high-precision segmentation, limiting it's usage in many real-world applications.
In this work, we explored transferring SAM into the domain of high-resolution images and proposed Pi-SAM. Compared to the original SAM and its variants, Pi-SAM demonstrates the following superiorities:
**Firstly**, Pi-SAM possesses a strong perception capability for the extremely fine details in high-resolution images, enabling it to generate high-precision segmentation masks. As a result,Pi-SAM significantly surpasses previous methods in four high-resolution datasets.
**Secondly**, Pi-SAM supports more precise user interactions. In addition to the native promptable ability of SAM, Pi-SAM allows users to interactively refine the segmentation predictions simply by clicking. While the original SAM fails to achieve this on high-resolution images.
**Thirdly**, building upon SAM, Pi-SAM freezes all its original parameters and introduces very few additional parameters and computational costs to achieve the above performance. This ensures highly efficient model fine-tuning while also retaining the powerful semantic information contained in the original SAM.
Segment Anything with Precise Interaction
Mengzhen Liu Mengyu Wang Henghui Ding Yilong Xu Yao Zhao Yunchao Wei