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
IITK at SemEval-2024 Task 4: Hierarchical Embeddings for Detection of Persuasion Techniques in Memes
exploration-lab/iitk-semeval-2024-task-4-pursuasion-techniques
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Memes are one of the most popular types of content used in an online disinformation campaign.
BCAmirs at SemEval-2024 Task 4: Beyond Words: A Multimodal and Multilingual Exploration of Persuasion in Memes
amirabaskohi/beyond-words-a-multimodal-exploration-of-persuasion-in-memes
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Memes, combining text and images, frequently use metaphors to convey persuasive messages, shaping public opinion.
Mixture of Mixups for Multi-label Classification of Rare Anuran Sounds
Multi-label imbalanced classification poses a significant challenge in machine learning, particularly evident in bioacoustics where animal sounds often co-occur, and certain sounds are much less frequent than others.
Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry
A dataset containing over 20, 000 high-resolution coral images of different health conditions and stressors was constructed based on the field survey.
Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery
techmn/satmae_pp
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Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data.
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
wzh9969/hiadv
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Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure.
What limits performance of weakly supervised deep learning for chest CT classification?
First, we examined model tolerance for noisy data by incrementally increasing error in the labels within the training data.
A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts
In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts.
In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
JMA: a General Algorithm to Craft Nearly Optimal Targeted Adversarial Example
guowei-cn/JMA--A-General-Close-to-Optimal-Targeted-Adversarial-Attack-with-Improved-Efficiency
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Most of the approaches proposed so far to craft targeted adversarial examples against Deep Learning classifiers are highly suboptimal and typically rely on increasing the likelihood of the target class, thus implicitly focusing on one-hot encoding settings.
