Foundation Model
Foundation models are large, pre-trained AI models designed to generalize across diverse tasks and datasets, offering a powerful alternative to task-specific models. Current research emphasizes adapting these models to various domains, including healthcare (e.g., medical image analysis, EEG interpretation), scientific applications (e.g., genomics, weather forecasting), and robotics, often employing architectures like transformers and mixtures of experts with innovative gating functions. This approach promises to improve efficiency and accuracy in numerous fields by leveraging the knowledge embedded within these powerful models, streamlining data analysis and enabling new applications previously hindered by data scarcity or computational limitations.
Papers
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Ryutaro Tanno, David G. T. Barrett, Andrew Sellergren, Sumedh Ghaisas, Sumanth Dathathri, Abigail See, Johannes Welbl, Karan Singhal, Shekoofeh Azizi, Tao Tu, Mike Schaekermann, Rhys May, Roy Lee, SiWai Man, Zahra Ahmed, Sara Mahdavi, Yossi Matias, Joelle Barral, Ali Eslami, Danielle Belgrave, Vivek Natarajan, Shravya Shetty, Pushmeet Kohli, Po-Sen Huang, Alan Karthikesalingam, Ira Ktena
Towards A Foundation Model For Trajectory Intelligence
Alameen Najjar
One-Shot Open Affordance Learning with Foundation Models
Gen Li, Deqing Sun, Laura Sevilla-Lara, Varun Jampani
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Yan Kang, Tao Fan, Hanlin Gu, Xiaojin Zhang, Lixin Fan, Qiang Yang
Federated Fine-Tuning of Foundation Models via Probabilistic Masking
Vasileios Tsouvalas, Yuki Asano, Aaqib Saeed
Finding Foundation Models for Time Series Classification with a PreText Task
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
Robot Learning in the Era of Foundation Models: A Survey
Xuan Xiao, Jiahang Liu, Zhipeng Wang, Yanmin Zhou, Yong Qi, Qian Cheng, Bin He, Shuo Jiang
On the Out of Distribution Robustness of Foundation Models in Medical Image Segmentation
Duy Minh Ho Nguyen, Tan Ngoc Pham, Nghiem Tuong Diep, Nghi Quoc Phan, Quang Pham, Vinh Tong, Binh T. Nguyen, Ngan Hoang Le, Nhat Ho, Pengtao Xie, Daniel Sonntag, Mathias Niepert
EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge
Bufang Yang, Lixing He, Neiwen Ling, Zhenyu Yan, Guoliang Xing, Xian Shuai, Xiaozhe Ren, Xin Jiang
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Xidong Wu, Wan-Yi Lin, Devin Willmott, Filipe Condessa, Yufei Huang, Zhenzhen Li, Madan Ravi Ganesh
GlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy
Hongyang Jiang, Mengdi Gao, Zirong Liu, Chen Tang, Xiaoqing Zhang, Shuai Jiang, Wu Yuan, Jiang Liu