Tool learning with foundation models

Y Qin, S Hu, Y Lin, W Chen, N Ding, G Cui… - ACM Computing …, 2024 - dl.acm.org
Humans possess an extraordinary ability to create and utilize tools. With the advent of
foundation models, artificial intelligence systems have the potential to be equally adept in …

[HTML][HTML] Review of large vision models and visual prompt engineering

J Wang, Z Liu, L Zhao, Z Wu, C Ma, S Yu, H Dai… - Meta-Radiology, 2023 - Elsevier
Visual prompt engineering is a fundamental methodology in the field of visual and image
artificial general intelligence. As the development of large vision models progresses, the …

Multimodal foundation models: From specialists to general-purpose assistants

C Li, Z Gan, Z Yang, J Yang, L Li… - … and Trends® in …, 2024 - nowpublishers.com
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

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) …

Segment anything model for medical image segmentation: Current applications and future directions

Y Zhang, Z Shen, R Jiao - Computers in Biology and Medicine, 2024 - Elsevier
Due to the inherent flexibility of prompting, foundation models have emerged as the
predominant force in the fields of natural language processing and computer vision. The …

Ma-sam: Modality-agnostic sam adaptation for 3d medical image segmentation

C Chen, J Miao, D Wu, A Zhong, Z Yan, S Kim… - Medical Image …, 2024 - Elsevier
Abstract The Segment Anything Model (SAM), a foundation model for general image
segmentation, has demonstrated impressive zero-shot performance across numerous …

Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation

R Jiao, Y Zhang, L Ding, B Xue, J Zhang, R Cai… - Computers in Biology …, 2024 - Elsevier
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

MedLSAM: Localize and segment anything model for 3D CT images

W Lei, W Xu, K Li, X Zhang, S Zhang - Medical Image Analysis, 2025 - Elsevier
Recent advancements in foundation models have shown significant potential in medical
image analysis. However, there is still a gap in models specifically designed for medical …

A comprehensive survey on segment anything model for vision and beyond

C Zhang, L Liu, Y Cui, G Huang, W Lin, Y Yang… - arxiv preprint arxiv …, 2023 - arxiv.org
Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the
ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence …

Surgicalsam: Efficient class promptable surgical instrument segmentation

W Yue, J Zhang, K Hu, Y **a, J Luo… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised
image segmentation. To apply SAM to surgical instrument segmentation, a common …