Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation
arxiv(2024)
摘要
Multi-class multi-instance segmentation is the task of identifying masks for
multiple object classes and multiple instances of the same class within an
image. The foundational Segment Anything Model (SAM) is designed for promptable
multi-class multi-instance segmentation but tends to output part or sub-part
masks in the "everything" mode for various real-world applications. Whole
object segmentation masks play a crucial role for indoor scene understanding,
especially in robotics applications. We propose a new domain invariant
Real-to-Simulation (Real-Sim) fine-tuning strategy for SAM. We use object
images and ground truth data collected from Ai2Thor simulator during
fine-tuning (real-to-sim). To allow our Segment Any Object Model (SAOM) to work
in the "everything" mode, we propose the novel nearest neighbour assignment
method, updating point embeddings for each ground-truth mask. SAOM is evaluated
on our own dataset collected from Ai2Thor simulator. SAOM significantly
improves on SAM, with a 28
frequently-seen indoor object classes. Moreover, our Real-to-Simulation
fine-tuning strategy demonstrates promising generalization performance in real
environments without being trained on the real-world data (sim-to-real). The
dataset and the code will be released after publication.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要