SOTAVerified

Multi-Label Learning

Multi-label learning (MLL) is a generalization of the binary and multi-category classification problems and deals with tagging a data instance with several possible class labels simultaneously [1]. Each of the assigned labels conveys a specific semantic relationship with the multi-label data instance [2, 3]. Multi-label learning has continued to receive a lot of research interest due to its practical application in many real-world problems such as recommender systems [4], image annotation [5], and text classification [6].

References:

  1. Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)

  2. Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837

  3. Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38

  4. Bogaert M, Lootens J, Van den Poel D, Ballings M (2019) Evaluating multi-label classifiers and recommender systems in the financial service sector. Eur J Oper Res 279(2):620– 634

  5. Jing L, Shen C, Yang L, Yu J, Ng MK (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 26(10):4612–4625

  6. Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 299 papers

TitleStatusHype
Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering—0
Variational Label Enhancement—0
View-Category Interactive Sharing Transformer for Incomplete Multi-View Multi-Label Learning—0
Weakly Supervised Dense Video Captioning—0
Weakly-Supervised Multi-Person Action Recognition in 360^ Videos—0
Weakly Supervised Person Re-Identification—0
Zero-Shot Learning Over Large Output Spaces : Utilizing Indirect Knowledge Extraction from Large Language Models—0
A Consistent Lebesgue Measure for Multi-label Learning—0
A Cross-Conformal Predictor for Multi-label Classification—0
Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing—0
Active Refinement for Multi-Label Learning: A Pseudo-Label Approach—0
A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem—0
Advancing Head and Neck Cancer Survival Prediction via Multi-Label Learning and Deep Model Interpretation—0
Adversarial Paritial Multi-label Learning—0
Adversarial Partial Multi-Label Learning—0
Adversarial Training for Relation Extraction—0
A Multi-label Multi-hop Relation Detection Model based on Relation-aware Sequence Generation—0
A Multi-Task Gradient Descent Method for Multi-Label Learning—0
On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
APLenty: annotation tool for creating high-quality datasets using active and proactive learning—0
A Procedural Texture Generation Framework Based on Semantic Descriptions—0
A Pseudo-Label Method for Coarse-to-Fine Multi-Label Learning with Limited Supervision—0
A Self-Paced Regularization Framework for Multi-Label Learning—0
A Similarity-based Framework for Classification Task—0
A Simple Approach to Image Tilt Correction with Self-Attention MobileNet for Smartphones—0
A Study on the Autoregressive and non-Autoregressive Multi-label Learning—0
A Submodular Feature-Aware Framework for Label Subset Selection in Extreme Classification Problems—0
A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends—0
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning—0
Asymptotic consistency and order specification for logistic classifier chains in multi-label learning—0
Attentional Multilabel Learning over Graphs: A Message Passing Approach—0
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments—0
Bayesian Network Based Label Correlation Analysis For Multi-label Classifier Chain—0
Bidirectional Loss Function for Label Enhancement and Distribution Learning—0
Can Class-Priors Help Single-Positive Multi-Label Learning?—0
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise—0
Characterizing the Evasion Attackability of Multi-label Classifiers—0
Classifier Chains: A Review and Perspectives—0
Collaboration based Multi-Label Learning—0
Compact Learning for Multi-Label Classification—0
Comparing and combining classifiers for self-taught vocal interfaces—0
Component-Wise Boosting of Targets for Multi-Output Prediction—0
Context-Based Semantic-Aware Alignment for Semi-Supervised Multi-Label Learning—0
Copula Multi-label Learning—0
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning—0
The Overlooked Classifier in Human-Object Interaction Recognition—0
Deep Determinantal Point Process for Large-Scale Multi-Label Classification—0
Deep Learning for Multi-Label Learning: A Comprehensive Survey—0
Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SADCLCF179.8—Unverified