Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Adapting Vision-Language Models from Iconic to Inclusive for Multi-Label Recognition Without Labels
Understanding multi-label images remains a challenging task in computer vision. With the rapid progress of vision-language multimodal learning, vision-language models (VLMs) enable zero-shot recognition without labeled d…
Multi-Label LearningPrincipled Algorithms for Optimizing Generalized Metrics in Multi-Label Learning
Many real-world classification tasks require predicting multiple labels per instance, necessitating the optimization of complex evaluation metrics such as the $F$-measure and Jaccard index. While the Empirical Utility Ma…
Multi-Label LearningCoherent Hierarchical Multi-Label Learning to Defer for Medical Imaging
Learning to Defer (L2D) enables a model to predict autonomously or defer to an expert, but prior work largely assumes flat label spaces. We study the first L2D setting with hierarchical multi-label decisions, motivated b…
Multi-Label LearningHow Label Imbalance Shapes Geometry: A General Spectral Analysis of Multi-Label Neural Collapse
This work investigates the phenomenon of Neural Collapse (NC) in multi-label classification, extending its conceptual framework from multi-class learning to general correlated and imbalanced multi-label settings. Althoug…
Multi-Label ClassificationMulti-Label LearningFedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw data. However, model…
Multi-Label LearningFeature-Label Modal Alignment for Robust Partial Multi-Label Learning
In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrupts the correspondence between features a…
Multi-Label LearningBringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both relevant and irrelevant labels. While clus…
Multi-Label LearningIncomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation
Although multi-view multi-label learning has been extensively studied, research on the dual-missing scenario, where both views and labels are incomplete, remains largely unexplored. Existing methods mainly rely on contra…
Multi-Label ClassificationRepresentation LearningMulti-Label LearningContrastive LearningA Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
Transcription factors (TFs) regulate gene expression through complex and co-operative mechanisms. While many TFs act together, the logic underlying TFs binding and their interactions is not fully understood yet. Most cur…
Multi-Label ClassificationBinary ClassificationMulti-Label LearningEarly Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning has shown promise in predicting the single adverse event, several key challeng…
Multi-Label LearningSEHFS: Structural Entropy-Guided High-Order Correlation Learning for Multi-View Multi-Label Feature Selection
In recent years, multi-view multi-label learning (MVML) has attracted extensive attention due to its close alignment to real-world scenarios. Information-theoretic methods have gained prominence for learning nonlinear co…
Multi-Label LearningA Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining
The long-tail distribution, where a few head labels dominate while rare tail labels abound, poses a persistent challenge for large-scale Multi-Label Classification (MLC) in real-world data mining applications. Existing r…
Multi-Label ClassificationMulti-Label LearningHandling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning
Chest X-Ray (CXR) classification in clinical practice is often limited by imperfect supervision, arising from (i) extreme long-tailed multi-label disease distributions and (ii) missing annotations for rare or previously …
Multi-Label ClassificationMulti-Label LearningZero-Shot LearningImproving Detection of Rare Nodes in Hierarchical Multi-Label Learning
In hierarchical multi-label classification, a persistent challenge is enabling model predictions to reach deeper levels of the hierarchy for more detailed or fine-grained classifications. This difficulty partly arises fr…
Hierarchical Multi-label ClassificationMulti-Label LearningFrom Ambiguity to Action: A POMDP Perspective on Partial Multi-Label Ambiguity and Its Horizon-One Resolution
In partial multi-label learning (PML), the true labels are unobserved, which makes label disambiguation important but difficult. A key challenge is that ambiguous candidate labels can propagate errors into downstream tas…
Reinforcement LearningMulti-Label LearningFeature EngineeringTeachPro: Multi-Label Qualitative Teaching Evaluation via Cross-View Graph Synergy and Semantic Anchored Evidence Encoding
Standardized Student Evaluation of Teaching often suffer from low reliability, restricted response options, and response distortion. Existing machine learning methods that mine open-ended comments usually reduce feedback…
Multi-Label LearningAdaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classification
Multi-view multi-label learning frequently suffers from simultaneous feature absence and incomplete annotations, due to challenges in data acquisition and cost-intensive supervision. To tackle the complex yet highly prac…
Multi-Label ClassificationRepresentation LearningMulti-Label LearningThe Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks
Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dependencies and are not specifically adapt…
Multi-Label ClassificationMulti-Label LearningFeature EngineeringCode GenerationCausal Invariance and Counterfactual Learning Driven Cooperative Game for Multi-Label Classification
Multi-label classification (MLC) remains vulnerable to label imbalance, spurious correlations, and distribution shifts, challenges that are particularly detrimental to rare label prediction. To address these limitations,…
Multi-Label ClassificationMulti-Label LearningCausal InferenceDiCaP: Distribution-Calibrated Pseudo-labeling for Semi-Supervised Multi-Label Learning
Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model's performance. While pseudo-labeling has …
Multi-Label LearningContrastive Learning