Papers Few-Shot Learning
“Few-Shot Learning” 태그가 달린 논문 3,293편 · 필터 해제
ALPINE: Adaptive Localization for Parameter- and Sample-Efficient Few-Shot Learning
Few-shot learning research is predominantly evaluated on accuracy alone, with limited attention to the parameter and training-sample budgets required to reach that accuracy - a real constraint for practitioners without l…
Few-Shot Image ClassificationFew-Shot LearningEnhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation
This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA). PLA aims to simplify technical medical language, bridging a critical gap betw…
Reading ComprehensionText SimplificationFew-Shot LearningSample-Conditioned Representation Selection for Audio Few-Shot Learning
Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting representation shift is concentrated and class dependent: for ResNet12, the …
Few-Shot LearningLearning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs
Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-free coverage guarantees. However, standard conformal prediction relies…
Few-Shot LearningDecoupled I/O-Dominant Pipelines for Large-Scale Whole-Slide Image Embedding Extraction
Whole-slide images (WSIs) are central to computational pathology but are prohibitively large, making patch-based processing the practical unit for foundation model inference. At scale, however, generating and handling ma…
Few-Shot LearningMetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact, specialized forecasters are more desira…
Computational EfficiencyTime Series ForecastingFew-Shot LearningEvaluating and Explaining Prompt Sensitivity of LLMs Using Interactions
The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelevant changes in prompts can cause dramatic fluctuations in performance, a phenomen…
Few-Shot LearningCan Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach
Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of their underlying causes at the level of pilot control behavior. Existing explain…
Feature EngineeringFeature ImportanceFew-Shot LearningAnalysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks
Background and Context: Question and inquiry are integral parts of knowledge seeking and learning. Despite their importance, students tend not to ask enough questions in the classroom. However, studies have shown that st…
Few-Shot LearningBayesian adaptively-weighted ensembles for few-shot abdominal segmentation
Few-shot learning has emerged as a promising approach for anatomical segmentation when labelled data are scarce. However, different few-shot learning algorithms exhibit complementary strengths and weaknesses, with perfor…
Few-Shot LearningKING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots
Kinematic models provide reliable motion constraints for odometry estimation in featureless environments, where exteroceptive sensing degrades and IMU integration drifts. Learning-based kinematic models can achieve more …
Graph Neural NetworkFew-Shot LearningToward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offerin…
Federated LearningFew-Shot LearningGraph LearningMQAdapter: Multi-Modal Quantum Adapter for Coarse-to-Fine VLM Fine-tuning
Large-scale Vision-Language Models have demonstrated impressive transfer learning capabilities across a wide range of tasks. For few-shot classification, we observe that VLMs exhibit a notable ability to filter candidate…
Few-Shot LearningTransfer LearningComparative Study of Domain-adapted VLMs for General Document Visual Question Answering
Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vision-Language Models (VLMs) have shown r…
Visual Question AnsweringFew-Shot LearningModular Foundation Models for Time-Series Perception in Digital Twins
Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data. However, most exis…
Self-Supervised LearningFew-Shot LearningNeuron-Aware Active Few-Shot Learning for LLMs
Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while p…
Text ClassificationFew-Shot LearningPrototype Memory-Guided Training-Free Anomaly Classification and Localization in Prenatal Ultrasound
Prenatal anomaly classification and localization is of critical importance for fetal health and pregnancy management. Although ultrasound (US) is the primary modality for prenatal screening, accurate diagnosis remains ch…
Anomaly ClassificationFew-Shot LearningKalman Prototypical Networks for Few-shot Fault Detection in Combined Cycle Gas Turbines
Combined-cycle gas turbines (CCGTs) play a key role in modern power generation, offering both high efficiency and reduced environmental impact. However, their complex thermo-fluid and mechanical interactions complicate f…
Few-Shot LearningFault DiagnosisA welding penetration prediction model for laser welding process based on self-supervised learning using physics-informed neural networks
The laser welding full-penetration is of critical importance, as it constitutes one of the fundamental factors in achieving defect-free welded joints. Accurate prediction of the penetration state is therefore essential f…
Self-Supervised LearningContrastive LearningImage AugmentationFew-Shot LearningAdaptive Hebbian Memory Routing in Vision Transformers for Few-Shot Learning
Few-shot image recognition requires models to adapt to new classes from a small labeled support set. Hebbian fast-weight memory can provide temporary associative information during an episode, but fixed memory behavior m…
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