Papers Multi-Label Learning
“Multi-Label Learning” 태그가 달린 논문 334편 · 필터 해제
Graph based Label Enhancement for Multi-instance Multi-label learning
Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The …
image-classificationImage ClassificationMulti-Label LearningLight-weight Deep Extreme Multilabel Classification
Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels. Hence, scalability of the classifier with increasing label dimension is an important …
ClassificationMulti-Label LearningMER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia. The challenge focuses on system robustness and consists of three distinct tracks: (1) MER-MULTI, where participants a…
Emotion RecognitionMulti-Label LearningMultimodal Emotion RecognitionReliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification
As a cross-topic of multi-view learning and multi-label classification, multi-view multi-label classification has gradually gained traction in recent years. The application of multi-view contrastive learning has further …
ClassificationContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learni…
ClassificationDecoderMissing LabelsMulti-Label Classification+3DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification
In recent years, multi-view multi-label learning has aroused extensive research enthusiasm. However, multi-view multi-label data in the real world is commonly incomplete due to the uncertain factors of data collection an…
Contrastive LearningMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers
As we all know, multi-view data is more expressive than single-view data and multi-label annotation enjoys richer supervision information than single-label, which makes multi-view multi-label learning widely applicable f…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningRepresentation LearningPushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels
In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternativ…
Multi-Label LearningTransductive Matrix Completion with Calibration for Multi-Task Learning
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful method in multi-label learning. In this…
Matrix CompletionMulti-Label LearningMulti-Task LearningMulti-label learning with missing labels using sparse global structure for label-specific features
Multi-label learning associates a given data instance with one or several class labels. A frequent problem with real life multi-label datasets is the lack of complete label information. Incomplete labels increase model c…
Missing LabelsMulti-Label LearningAsymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning
Learning with noisy-labels has become an important research topic in computer vision where state-of-the-art (SOTA) methods explore: 1) prediction disagreement with co-teaching strategy that updates two models when they d…
Learning with noisy labelsMulti-Label LearningPredictionProbing Sentiment-Oriented Pre-Training Inspired by Human Sentiment Perception Mechanism
Pre-training of deep convolutional neural networks (DCNNs) plays a crucial role in the field of visual sentiment analysis (VSA). Most proposed methods employ the off-the-shelf backbones pre-trained on large-scale obj…
Multi-Label LearningSentiment AnalysisRLSEP: Learning Label Ranks for Multi-label Classification
Multi-label ranking maps instances to a ranked set of predicted labels from multiple possible classes. The ranking approach for multi-label learning problems received attention for its success in multi-label classificati…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningLearning Disentangled Label Representations for Multi-label Classification
Although various methods have been proposed for multi-label classification, most approaches still follow the feature learning mechanism of the single-label (multi-class) classification, namely, learning a shared image fe…
AttributeClassificationDisentanglementMulti-class Classification+5A Cross-Conformal Predictor for Multi-label Classification
Unlike the typical classification setting where each instance is associated with a single class, in multi-label learning each instance is associated with multiple classes simultaneously. Therefore the learning task in th…
ClassificationConformal PredictionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Private Multi-Winner Voting for Machine Learning
Private multi-winner voting is the task of revealing $k$-hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine learning literature despite its prevalence …
Multi-Label LearningPrivacy PreservingTabMixer: Excavating Label Distribution Learning with Small-scale Features
Label distribution learning (LDL) differs from multi-label learning which aims at representing the polysemy of instances by transforming single-label values into descriptive degrees. Unfortunately, the feature space of t…
DescriptiveInductive BiasMulti-Label LearningFood Ingredients Recognition through Multi-label Learning
The ability to recognize various food-items in a generic food plate is a key determinant for an automated diet assessment system. This study motivates the need for automated diet assessment and proposes a framework to ac…
DecoderFood RecognitionMulti-Label LearningA Survey on Extreme Multi-label Learning
Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implici…
Multi-Label LearningSurveyLabel Distribution Learning via Implicit Distribution Representation
In contrast to multi-label learning, label distribution learning characterizes the polysemy of examples by a label distribution to represent richer semantics. In the learning process of label distribution, the training d…
Multi-Label LearningRepresentation Learning