Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
MetaChest: Generalized few-shot learning of pathologies from chest X-rays
The limited availability of annotated data presents a major challenge for applying deep learning methods to medical image analysis. Few-shot learning methods aim to recognize new classes from only a small number of label…
Generalized Few-Shot LearningMulti-Label ClassificationTransfer LearningAutoIntent: AutoML for Text Classification
AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision thr…
Multi-Label ClassificationIntent ClassificationText ClassificationSemantic F1 Scores: Fair Evaluation Under Fuzzy Class Boundaries
We propose Semantic F1 Scores, novel evaluation metrics for subjective or fuzzy multi-label classification that quantify semantic relatedness between predicted and gold labels. Unlike the conventional F1 metrics that tre…
Multi-Label ClassificationOverview of PlantCLEF 2025: Multi-Species Plant Identification in Vegetation Quadrat Images
Quadrat images are essential for ecological studies, as they enable standardized sampling, the assessment of plant biodiversity, long-term monitoring, and large-scale field campaigns. These images typically cover an area…
Multi-Label ClassificationDual-View Alignment Learning with Hierarchical-Prompt for Class-Imbalance Multi-Label Classification
Real-world datasets often exhibit class imbalance across multiple categories, manifesting as long-tailed distributions and few-shot scenarios. This is especially challenging in Class-Imbalanced Multi-Label Image Classifi…
Multi-Label Image ClassificationFew-Shot Image ClassificationMulti-Label ClassificationObject RecognitionDiffusion-Based Cross-Modal Feature Extraction for Multi-Label Classification
Multi-label classification has broad applications and depends on powerful representations capable of capturing multi-label interactions. We introduce \textit{Diff-Feat}, a simple but powerful framework that extracts inte…
Multi-Label ClassificationOverview of PlantCLEF 2024: multi-species plant identification in vegetation plot images
Plot images are essential for ecological studies, enabling standardized sampling, biodiversity assessment, long-term monitoring and remote, large-scale surveys. Plot images are typically fifty centimetres or one square m…
Multi-Label ClassificationLanguage-Instructed Reasoning for Group Activity Detection via Multimodal Large Language Model
Group activity detection (GAD) aims to simultaneously identify group members and categorize their collective activities within video sequences. Existing deep learning-based methods develop specialized architectures (e.g.…
Multi-Label ClassificationActivity DetectionEarly Prediction of Multi-Label Care Escalation Triggers in the Intensive Care Unit Using Electronic Health Records
Intensive Care Unit (ICU) patients often present with complex, overlapping signs of physiological deterioration that require timely escalation of care. Traditional early warning systems, such as SOFA or MEWS, are limited…
Multi-Label ClassificationRespiratory FailureTerm2Note: Synthesising Differentially Private Clinical Notes from Medical Terms
Training data is fundamental to the success of modern machine learning models, yet in high-stakes domains such as healthcare, the use of real-world training data is severely constrained by concerns over privacy leakage. …
Multi-Label ClassificationText GenerationMulti-pathology Chest X-ray Classification with Rejection Mechanisms
Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-occurring pathologies must be detected s…
Multi-Label ClassificationMulti-Label Transfer Learning in Non-Stationary Data Streams
Label concepts in multi-label data streams often experience drift in non-stationary environments, either independently or in relation to other labels. Transferring knowledge between related labels can accelerate adaptati…
Multi-Label ClassificationTransfer LearningSemantic-Aware Edge Intelligence for UAV Handover in 6G Networks
6G wireless networks aim to exploit semantic awareness to optimize radio resources. By optimizing the transmission through the lens of the desired goal, the energy consumption of transmissions can also be reduced, and th…
Multi-Label ClassificationSemantic CommunicationMCANet: A Multi-Scale Class-Specific Attention Network for Multi-Label Post-Hurricane Damage Assessment using UAV Imagery
Rapid and accurate post-hurricane damage assessment is vital for disaster response and recovery. Yet existing CNN-based methods struggle to capture multi-scale spatial features and to distinguish visually similar or co-o…
Multi-Label ClassificationKnowledge GraphsPLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series
Multivariate time series (MTS) are ubiquitous in domains such as healthcare, climate science, and industrial monitoring, but their high dimensionality, limited labeled data, and non-stationary nature pose significant cha…
Multi-Label ClassificationSelf-Supervised LearningRepresentation LearningAnomaly DetectionQuantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation
Large language models have demonstrated remarkable capabilities in biomedical natural language processing, yet their rapid growth in size and computational requirements present a major barrier to adoption in healthcare s…
Multi-Label ClassificationRelation ExtractionQuestion AnsweringImproving Narrative Classification and Explanation via Fine Tuned Language Models
Understanding covert narratives and implicit messaging is essential for analyzing bias and sentiment. Traditional NLP methods struggle with detecting subtle phrasing and hidden agendas. This study tackles two key challen…
Multi-Label ClassificationSemantic RetrievalAnisotropic Fourier Features for Positional Encoding in Medical Imaging
The adoption of Transformer-based architectures in the medical domain is growing rapidly. In medical imaging, the analysis of complex shapes - such as organs, tissues, or other anatomical structures - combined with the o…
Multi-Label ClassificationScaling behavior of large language models in emotional safety classification across sizes and tasks
Understanding how large language models (LLMs) process emotionally sensitive content is critical for building safe and reliable systems, particularly in mental health contexts. We investigate the scaling behavior of LLMs…
Multi-Label ClassificationExamination of PCA Utilisation for Multilabel Classifier of Multispectral Images
This paper investigates the utility of Principal Component Analysis (PCA) for multi-label classification of multispectral images using ResNet50 and DINOv2, acknowledging the high dimensionality of such data and the assoc…
Multi-Label ClassificationDimensionality ReductionImage Classification