  Powered by massive curated training data, Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios
with the guidance of prompts. However, the vanilla SAM is class agnostic and
heavily relies on user-provided prompts to segment objects of interest.
Adapting this method to diverse tasks is crucial for accurate target
identification and to avoid suboptimal segmentation results. In this paper, we
propose a novel framework, termed AlignSAM, designed for automatic prompting
for aligning SAM to an open context through reinforcement learning. Anchored by
an agent, AlignSAM enables the generality of the SAM model across diverse
downstream tasks while keeping its parameters frozen. Specifically, AlignSAM
initiates a prompting agent to iteratively refine segmentation predictions by
interacting with the foundational model. It integrates a reinforcement learning
policy network to provide informative prompts to the foundational models.
Additionally, a semantic recalibration module is introduced to provide
fine-grained labels of prompts, enhancing the model's proficiency in handling
tasks encompassing explicit and implicit semantics. Experiments conducted on
various challenging segmentation tasks among existing foundation models
demonstrate the superiority of the proposed AlignSAM over state-of-the-art
approaches. Project page:
\url{https://github.com/Duojun-Huang/AlignSAM-CVPR2024}.
