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This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy...
Data quality has become a decisive foundation for large foundation models, shaping their capability, reliability, alignment, and real-world applicability. Data Engineering for Large Foundation Models: A Handbook provides a systematic and practice-oriented guide to data engineering for foundation mod...
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed fo...
This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization–model–data tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to ...
This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy...
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed fo...
This book constitutes the refereed proceedings from the Second International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Mar...
This book constitutes the refereed proceedings from the Third International Workshop on Foundation Models for General Medical AI, MedAGI 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Kore...
This book constitutes the refereed proceedings from the Second International Workshop on Foundation Models for General Medical AI, MedAGI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Mar...
This book constitutes the refereed proceedings from the Third International Workshop on Foundation Models for General Medical AI, MedAGI 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Kore...
This book provides a systematic and in-depth introduction to machine unlearning (MU) for foundation models, framed through an optimization–model–data tri-design perspective and complemented by assessments and applications. As foundation models are continuously adapted and reused, the ability to ...
This book constitutes the proceedings of the CVPR 2025 Challenge on Foundation Models for 3D Biomedical Image Segmentation, MedSegFM 2025, held in Nashville, TN, USA, during June 11–15, 2025.
The 13 full papers included in this book were carefully reviewed and selected from 19 submissions. This co...
This book constitutes the proceedings of the CVPR 2025 Challenge on Foundation Models for 3D Biomedical Image Segmentation, MedSegFM 2025, held in Nashville, TN, USA, during June 11–15, 2025.
The 13 full papers included in this book were carefully reviewed and selected from 19 submissions. This co...
This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:
FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024. This LNAI volume focuses on the following topics:
Efficient Model Adaptation and Personalization, Data Heteroge...
This LNAI volume constitutes the post proceedings of International Federated Learning Workshops such as follows:
FL@FM-WWW 2024, FL@FM-ICME 2024, FL@FM-IJCAI 2024 and FL@FM-NeurIPS 2024. This LNAI volume focuses on the following topics:
Efficient Model Adaptation and Personalization, Data Heteroge...
This LNCS conference set constitutes the proceedings of the First Medical Image Segmentation Challenge, MedSAM on Laptop 2024, Held in Conjunction with CVPR 2024, in Seattle, WA, USA, held in June 2024.
The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions...
This LNCS conference set constitutes the proceedings of the First Medical Image Segmentation Challenge, MedSAM on Laptop 2024, Held in Conjunction with CVPR 2024, in Seattle, WA, USA, held in June 2024.
The 16 full papers presented were thoroughly reviewed and selected from the 200 submissions...
This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed fo...
Data quality has become a decisive foundation for large foundation models, shaping their capability, reliability, alignment, and real-world applicability. Data Engineering for Large Foundation Models: A Handbook provides a systematic and practice-oriented guide to data engineering for foundation mod...
This book constitutes the refereed proceedings of the 28th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2022, which was held in Aston, Birmingham, UK, during March 21-24, 2022. The 12 full and 7 short papers presented in this volume were c...