Multi-Label Classification
374 papers with code • 10 benchmarks • 28 datasets
Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is an extension of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.
Benchmarks
These leaderboards are used to track progress in Multi-Label Classification
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Libraries
Use these libraries to find Multi-Label Classification models and implementationsDatasets
MS COCO
CheXpert
NUS-WIDE
ChestX-ray14
MIMIC-CXR
PASCAL VOC 2007
ECHR
Friedman1
VizWiz-Classification
Subtasks
Latest papers with no code
Determined Multi-Label Learning via Similarity-Based Prompt
In this novel labeling setting, each training instance is associated with a \textit{determined label} (either "Yes" or "No"), which indicates whether the training instance contains the provided class label.
Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels
This paper focuses on open-ended video question answering, which aims to find the correct answers from a large answer set in response to a video-related question.
Neural Field Classifiers via Target Encoding and Classification Loss
We successfully propose a novel Neural Field Classifier (NFC) framework which formulates existing neural field methods as classification tasks rather than regression tasks.
Embedded Multi-label Feature Selection via Orthogonal Regression
Additionally, both global feature redundancy and global label relevancy information have been considered in the orthogonal regression model, which could contribute to the search for discriminative and non-redundant feature subsets in the multi-label data.
Automated Machine Learning for Multi-Label Classification
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand.
Improving Neural-based Classification with Logical Background Knowledge
We develop a new multi-scale methodology to evaluate how the benefits of a neurosymbolic technique evolve with the scale of the network.
Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance.
Hierarchical Multi-Label Classification of Online Vaccine Concerns
Vaccine concerns are an ever-evolving target, and can shift quickly as seen during the COVID-19 pandemic.
Deep Learning for Multi-Label Learning: A Comprehensive Survey
Multi-label learning is a rapidly growing research area that aims to predict multiple labels from a single input data point.
Consistent algorithms for multi-label classification with macro-at-$k$ metrics
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework.
