Multi-Label Learning

81 papers with code • 1 benchmarks • 7 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:

  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

Most implemented papers

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

Microsoft/FERPlus 3 Aug 2016

Crowd sourcing has become a widely adopted scheme to collect ground truth labels.

A Survey on Extreme Multi-label Learning

siddsax/XML-CNN 8 Oct 2022

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

xmc-aalto/bonsai 17 Apr 2019

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 18 Apr 2023

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

zkl20061823/DRML CVPR 2016

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

Refefer/fastxml 8 Sep 2016

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

MarcBS/food_ingredients_recognition 27 Jul 2017

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 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

tsoumakas/mulan 2 May 2019

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

amjadseyedi/SPMLD 8 Oct 2019

The major challenge of learning from multi-label data has arisen from the overwhelming size of label space which makes this problem NP-hard.