Multi-Label Learning
93 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
COCO (Common Objects in Context)
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.
