Multi-Label Learning
87 papers with code • 1 benchmarks • 8 datasets
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:
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Kumar, S., Rastogi, R., Low rank label subspace transformation for multi-label learning with missing labels. Information Sciences 596, 53–72 (2022)
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Zhang M-L, Zhou Z-H (2013) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819–1837
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Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surveys (CSUR) 47(3):1–38
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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
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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
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Chen Z, Ren J (2021) Multi-label text classification with latent word-wise label information. Appl Intell 51(2):966–979
Libraries
Use these libraries to find Multi-Label Learning models and implementationsDatasets
MS COCO
ExpW
BirdSong
Animal Kingdom
MIMIC Meme Dataset
Corn Seeds Dataset
Most implemented papers
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
A Survey on Extreme Multi-label Learning
siddsax/XML-CNN
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Multi-label learning has attracted significant attention from both academic and industry field in recent decades.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
zeroqiaoba/mer2023-baseline
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The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.
Deep Region and Multi-Label Learning for Facial Action Unit Detection
Region learning (RL) and multi-label learning (ML) have recently attracted increasing attentions in the field of facial Action Unit (AU) detection.
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.
Food Ingredients Recognition through Multi-label Learning
Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.
Learning to Separate Object Sounds by Watching Unlabeled Video
rhgao/Deep-MIML-Network
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ECCV 2018
Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods.
Self-Paced Multi-Label Learning with Diversity
The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard.
