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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
Complexity of Probabilistic Reasoning for Neurosymbolic Classification Techniques
Informed multi-label classification is a sub-field of neurosymbolic AI which studies how to leverage prior knowledge to improve neural classification systems.
Generative Resident Separation and Multi-label Classification for Multi-person Activity Recognition
This paper presents two models to address the problem of multi-person activity recognition using ambient sensors in a home.
Hierarchical Insights: Exploiting Structural Similarities for Reliable 3D Semantic Segmentation
Safety-critical applications like autonomous driving call for robust 3D environment perception algorithms which can withstand highly diverse and ambiguous surroundings.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
Supervised Gradual Machine Learning for Aspect Category Detection
Aspect Category Detection (ACD) aims to identify implicit and explicit aspects in a given review sentence.
Diverse and Tailored Image Generation for Zero-shot Multi-label Classification
Our approach introduces a novel image generation framework that produces multi-label synthetic images of unseen classes for classifier training.
Modeling Weather Uncertainty for Multi-weather Co-Presence Estimation
In this paper, we start with solid revisit of the physics definition of weather and how it can be described as a continuous machine learning and computer vision task.
Tabular Learning: Encoding for Entity and Context Embeddings
Examining the effect of different encoding techniques on entity and context embeddings, the goal of this work is to challenge commonly used Ordinal encoding for tabular learning.
Looking Beyond What You See: An Empirical Analysis on Subgroup Intersectional Fairness for Multi-label Chest X-ray Classification Using Social Determinants of Racial Health Inequities
In this study, we propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersectional groups in high-dimensional chest X- ray multi-label classification.
Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
This article argues that we can identify the events more accurately by leveraging the event taxonomy.
