Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy
  for Multi-Class Multi-Instance Segmentation
Mariia Khan Yue Qiu Yuren Cong Jumana Abu-Khalaf David Suter Bodo Rosenhahn
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% increase in mIoU and a 25% increase in mAcc for 54
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.