  The advent of large models, also known as foundation models, has
significantly transformed the AI research landscape, with models like Segment
Anything (SAM) achieving notable success in diverse image segmentation
scenarios. Despite its advancements, SAM encountered limitations in handling
some complex low-level segmentation tasks like camouflaged object and medical
imaging. In response, in 2023, we introduced SAM-Adapter, which demonstrated
improved performance on these challenging tasks. Now, with the release of
Segment Anything 2 (SAM2), a successor with enhanced architecture and a larger
training corpus, we reassess these challenges. This paper introduces
SAM2-Adapter, the first adapter designed to overcome the persistent limitations
observed in SAM2 and achieve new state-of-the-art (SOTA) results in specific
downstream tasks including medical image segmentation, camouflaged (concealed)
object detection, and shadow detection. SAM2-Adapter builds on the
SAM-Adapter's strengths, offering enhanced generalizability and composability
for diverse applications. We present extensive experimental results
demonstrating SAM2-Adapter's effectiveness. We show the potential and encourage
the research community to leverage the SAM2 model with our SAM2-Adapter for
achieving superior segmentation outcomes. Code, pre-trained models, and data
processing protocols are available at
http://tianrun-chen.github.io/SAM-Adaptor/
