Segment anything is not always perfect: An investigation of sam on different real-world applications

W Ji, J Li, Q Bi, T Liu, W Li, L Cheng - 2024 - Springer
Abstract Recently, Meta AI Research approaches a general, promptable segment anything
model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) …

RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model

K Chen, C Liu, H Chen, H Zhang, W Li… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Leveraging the extensive training data from SA-1B, the segment anything model (SAM)
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …

Samrs: Scaling-up remote sensing segmentation dataset with segment anything model

D Wang, J Zhang, B Du, M Xu, L Liu… - Advances in Neural …, 2024 - proceedings.neurips.cc
The success of the Segment Anything Model (SAM) demonstrates the significance of data-
centric machine learning. However, due to the difficulties and high costs associated with …