Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models
Extreme Multi-label Learning (XMC) is a task that allocates the most relevant labels for an instance from a predefined label set. Extreme Zero-shot XMC (EZ-XMC) is a special setting of XMC wherein no supervision is provi…
Language ModellingLarge Language ModelMulti-Label LearningZero-Shot LearningA Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends
In reality, data often exhibit associations with multiple labels, making multi-label learning (MLL) become a prominent research topic. The last two decades have witnessed the success of MLL, which is indispensable from c…
Multi-Label LearningMulti-label Class Incremental Emotion Decoding with Augmented Emotional Semantics Learning
Emotion decoding plays an important role in affective human-computer interaction. However, previous studies ignored the dynamic real-world scenario, where human experience a blend of multiple emotions which are increment…
class-incremental learningClass Incremental LearningIncremental LearningKnowledge Distillation+1Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation
A comprehensive and reliable survival prediction model is of great importance to assist in the personalized management of Head and Neck Cancer (HNC) patients treated with curative Radiation Therapy (RT). In this work, we…
Decision MakingMulti-Label LearningPredictionregression+1Pedestrian Attribute Recognition as Label-balanced Multi-label Learning
Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions le…
AttributeDiversityMulti-Label LearningPedestrian Attribute RecognitionBoosting Single Positive Multi-label Classification with Generalized Robust Loss
Multi-label learning (MLL) requires comprehensive multi-semantic annotations that is hard to fully obtain, thus often resulting in missing labels scenarios. In this paper, we investigate Single Positive Multi-label Learn…
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningMasked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages. In this paper, we…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningMULTI-VIEW LEARNINGMER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
Multimodal emotion recognition is an important research topic in artificial intelligence. Over the past few decades, researchers have made remarkable progress by increasing the dataset size and building more effective al…
Emotion RecognitionMulti-Label LearningMultimodal Emotion RecognitionPatch Spatio-Temporal Relation Prediction for Video Anomaly Detection
Video Anomaly Detection (VAD), aiming to identify abnormalities within a specific context and timeframe, is crucial for intelligent Video Surveillance Systems. While recent deep learning-based VAD models have shown promi…
Anomaly DetectionMulti-Label LearningPredictionRelation+3Determined Multi-Label Learning via Similarity-Based Prompt
In multi-label classification, each training instance is associated with multiple class labels simultaneously. Unfortunately, collecting the fully precise class labels for each training instance is time- and labor-consum…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningPrompt LearningProPML: Probability Partial Multi-label Learning
Partial Multi-label Learning (PML) is a type of weakly supervised learning where each training instance corresponds to a set of candidate labels, among which only some are true. In this paper, we introduce \our{}, a nove…
Multi-Label LearningWeakly-supervised LearningLearnability Gaps of Strategic Classification
In contrast with standard classification tasks, strategic classification involves agents strategically modifying their features in an effort to receive favorable predictions. For instance, given a classifier determining …
ClassificationMulti-Label LearningMIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning
MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilitie…
Multi-Label LearningTowards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
Algorithms designed for addressing typical supervised classification problems can only learn from a fixed set of samples and labels, making them unsuitable for the real world, where data arrives as a stream of samples of…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningA Consistent Lebesgue Measure for Multi-label Learning
Multi-label loss functions are usually non-differentiable, requiring surrogate loss functions for gradient-based optimisation. The consistency of surrogate loss functions is not proven and is exacerbated by the conflicti…
Multi-Label LearningDeep Learning for Multi-Label Learning: A Comprehensive Survey
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point. In the era of big data, tasks involving multi-label classification (MLC) or ranking present sig…
Deep LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Learning+1Semantic-Aware Multi-Label Adversarial Attacks
Despite its importance generating attacks for multi label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of l…
Multi-Label LearningView-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning
As a problem often encountered in real-world scenarios multi-view multi-label learning has attracted considerable research attention. However due to oversights in data collection and uncertainties in manual annotatio…
Multi-Label LearningTowards Calibrated Multi-label Deep Neural Networks
The problem of calibrating deep neural networks (DNNs) for multi-label learning is considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot classification are poorly calibrated. Man…
Multi-Label LearningMulti-label Learning from Privacy-Label
Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each instance, which can lead to the leakage of sensitive information (such as smoking, diseases, etc.) about the instances. However,…
Multi-Label Learning