This paper explores the transformative potential of Foundation Models (FMs) in agriculture, driven by the need for efficient and intelligent decision support systems in the face of growing global population and climate change. It begins by outlining...
Automated monitoring of cattle behavior through computer vision requires robust detection as a foundational step for tracking, re-identification, and behavior analysis. However, training accurate detection models typically demands extensive manually...
Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to cap...
Deep reinforcement learning (DRL) and large foundation models (FMs) have reshaped modern artificial intelligence (AI) by enabling systems that learn from interaction while leveraging broad generalization and multimodal reasoning capabilities. This su...
Foundation models (FMs) and vision-language models (VLMs) have emerged as a transformative paradigm in artificial intelligence, reshaping the landscape of medical image analysis and clinical decision support. Building upon large-scale pretraining on...
Vision-language foundation models have transformed medical image segmentation over the past three years. These models pair large image encoders with text prompts, so a single model can segment many anatomical structures, lesion types, and imaging mod...
Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action models, have opened new avenues for embodied AI in mobile service robotics. By combining fo...
Foundation models, known as the large-scale, pretrained models capable of generalizing across diverse tasks, have significantly advanced the field of medical image analysis. While most early applications focused on 2D modalities, the unique challenge...
Recent geospatial foundation models (GFMs) have shown strong performance for crop yield prediction across regions and spatial scales, but operational deployment requires reliable performance under distribution drift. In agricultural settings, distrib...
Foundation models have shown remarkable generalization ability for few-shot learning (FSL). However, their potential for remote sensing scene classification has not been fully explored. Existing methods mainly adapt a single vision–language mod...
Foundation models have achieved remarkable success across various domains, but still face critical challenges such as limited data availability, high computational requirements, and rapid knowledge obsolescence. To address these issues, we propose a...
We investigate the applicability of visual foundation models, a recent advancement in artificial intelligence, for archaeological remote sensing. In contrast to earlier approaches, we employ a strictly zero-shot methodology, testing the hypothesis th...
Tabular foundation models (TFMs) have moved tabular machine learning from per-dataset training towards amortised in-context inference, fitting a small-to-medium table in a single forward pass without a training run. The 2024–2026 release train,...
Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, th...
Data centers consumed 415 TWh of electricity in 2024, about 1.5% of global demand, and foundation model training and inference are a growing part of this load. As training and inference continue to grow, energy-efficient foundation models are becomin...
Foundation deformation prediction on expansive soils involves complex soil-structure interactions and environmental variability. This study develops foundation-specific hybrid modeling approaches for temporal deformation prediction using 974 days of...
In response to the urgent need for rapid and precise post-disaster damage evaluation, this study introduces the Visual Prompt Damage Evaluation (ViPDE) framework, a novel contrastive learning-based approach that leverages the embedded knowledge withi...
Combinatorial Route Optimization (CRO) problems, such as the Vehicle Routing Problem (VRP) or the Travelling Salesman Problem (TSP), are commonly seen in scheduling, logistics, and transportation. While current machine learning (ML) methods can overc...
Remote sensing image segmentation is a foundational task in Earth observation. With the rapid growth of remote sensing datasets in terms of scale, modality diversity, semantic openness, and spatio-temporal complexity, the field is evolving from task-...
Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by t...
Foundation models (FMs) have become a paradigm shift in the field of artificial intelligence, allowing one large-scale pretrained model to be customized for a broad set of downstream tasks using very little task-specific data. These models, which inc...
The rapid adoption of large language models (LLMs) and foundation models is reshaping machinery health monitoring. This shift moves the field beyond task-specific deep learning (DL) toward more generalist and multimodal intelligence. Addressing this...
Foundation models are increasingly influencing software engineering research and practice, yet their adoption across the software development life cycle remains uneven and insufficiently characterized. This paper presents a systematic review of 224 r...
Multimodal large language models (MLLMs) and vision–language models (VLMs) have rapidly entered medicine, demonstrating promising performance in clinical reasoning, radiology report generation, and visual question answering (VQA). However, many...
Background/Objectives: Glioma diagnosis increasingly depends on the integration of magnetic resonance imaging (MRI), molecular characteristics, and clinical information. Foundation models may support AI-assisted diagnosis through transferable represe...
Current machine learning approaches focusing on motor symptoms in Parkinson’s disease are commonly trained on small datasets and often lack generalizability from developmental setups to clinical applications. Foundation models using large, unla...
Automating activities in unstructured environments, such as construction sites, has been challenging due to unpredictable events, limiting robot adoption compared to structured settings. Recently, pre-trained foundation models, particularly Large Lan...
Panoptic maps enable robots to reason about both geometry and semantics. However, open-vocabulary models repeatedly produce closely related labels that split panoptic entities and degrade volumetric consistency. The proposed UPPM advances open-world...
Electroencephalogram (EEG)-based seizure classification remains challenging due to inter-subject variability and heterogeneous signal characteristics. Foundation models offer a promising alternative to dataset-specific training by leveraging pretrain...
Whole-slide image (WSI) compression is a fundamental requirement in digital pathology. Yet, current validation metrics, such as PSNR and pathologist agreement, were developed before the emergence of pathology foundation models (PFMs). PFMs capture fi...
Colorectal cancer (CRC) remains a leading global health challenge, with early and accurate diagnosis crucial for effective treatment. Histopathological evaluation, the current diagnostic gold standard, faces limitations including subjectivity, delaye...
This paper delivers a probabilistic attempt to prove that the selection of a random three-dimensional finite element (FE) model of a subsoil affects the computed settlements. Parametric analysis of a random soil block is conducted, assuming a variabl...
This paper presents a novel adversarial defense framework that strategically exploits the non-transferability of adversarial attacks across multi-modal foundation models. While Contrastive Language–Image Pre-training (CLIP) models demonstrate r...
Recently, time-series forecasting foundation models trained on large, diverse datasets have demonstrated robust zero-shot and few-shot capabilities. Given the ubiquity of time-series data in IoT, finance, and industrial applications, rigorous benchma...
Foundation models (FMs) are increasingly proposed as general-purpose solutions for computational pathology, with the potential to simplify clinical artificial intelligence deployment by reducing the need for task-specific architectures. However, thei...
Automated Valuation Models (AVMs) have progressed from hedonic and spatial statistics to machine-learning architectures, yet most remain optimized for one target, market, and observation period. This review asks whether the foundation model paradigm...
Foundation models are reshaping computational pathology by enabling scalable task-agnostic representations of histopathological whole-slide images (WSIs). Unlike earlier task-specific deep learning systems, pathology foundation models (PFMs) leverage...
Nowadays, the emergence of Foundation Models as a Service enables mobile users to access powerful capabilities such as inference and fine-tuning on demand and without incurring local computational overhead. This paper introduces a software-aware offl...
While traditional ensemble methods have dominated tabular intrusion detection systems (IDSs), recent advances in foundation models present new opportunities for enhanced cybersecurity applications. This paper presents a comprehensive multi-modal eval...
Deep learning has revolutionized medical image segmentation; however, the clinical deployment of state-of-the-art models is severely impeded by their quadratic computational complexity and substantial resource demands, particularly in multisensor and...
Artificial intelligence foundation models are increasingly deployed for prostate cancer Gleason grading, where GP3/GP4 distinction directly impacts treatment decisions (active surveillance vs. intervention). However, these models may achieve high val...
Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, cost...
Background: Thyroid cancer incidence continues to rise globally, yet current diagnostic methods, reliant on ultrasound-guided fine-needle aspiration, suffer from substantial inter-observer variability and indeterminate results. Objective: This review...
Large language models (LLMs) and time-series foundation models have advanced traffic forecasting, but almost exclusively on freeway sensors at five-minute, aggregate-flow resolution; their behavior on minute-resolution, movement-level counts at signa...
Cardiovascular ultrasound is a cornerstone of noninvasive cardiac and vascular assessment, yet conventional interpretation remains operator-dependent, variable, and limited in sensitivity for subclinical disease. Artificial intelligence (AI), particu...
Low-rank adaptation (LoRA) reduces training and communication costs in federated fine-tuning of foundation models, including large language models (LLMs). However, a given effective weight update generally admits multiple low-rank factorizations. Sha...
This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular compl...
The study presents a comprehensive framework for integrating foundation models (FMs), federated learning (FL), and Artificial Intelligence of Things (AIoT) technologies to enhance aircraft health monitoring systems (AHMSs). The proposed architecture...
Research in visual intelligence has shifted from task-specific convolutional networks trained on curated labels toward large-scale pretrained architectures that transfer across image, video, and language tasks with minimal fine-tuning. Existing surve...
Genome-scale searches for insect olfactory genes are vulnerable to rapid receptor divergence, tandem duplication, fragmented gene models, circular labels, and evolutionary leakage between training and test data. Sequence foundation models trained on...
of 323