SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

TL;DR

This paper introduces SAM-Adapter, a method to adapt the SAM model for challenging segmentation tasks like camouflage, shadow detection, and medical imaging, significantly improving performance without fine-tuning.

Contribution

We propose SAM-Adapter, a simple yet effective approach to enhance SAM's performance in difficult segmentation tasks by incorporating domain-specific prompts and knowledge.

Findings

SAM-Adapter outperforms existing models in camouflaged object detection.

SAM-Adapter achieves state-of-the-art results in shadow detection.

Medical image segmentation results are improved with SAM-Adapter.

Abstract

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose \textbf{SAM-Adapter}, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. By integrating task-specific knowledge with general knowledge learnt by the large model, SAM-Adapter can significantly elevate the performance of SAM in challenging tasks as shown in extensive experiments. We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection, shadow detection. We also tested polyp segmentation (medical image segmentation) and achieves better results. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more.