Multi-Label Classification
375 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
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.
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training
As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags.
Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media
In the realm of public health, vaccination stands as the cornerstone for mitigating disease risks and controlling their proliferation.
Multi-Label Classification of COVID-Tweets Using Large Language Models
Vaccination is important to minimize the risk and spread of various diseases.
Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise
Multi-label classification poses challenges due to imbalanced and noisy labels in training data.
Language-Guided Transformer for Federated Multi-Label Classification
Nevertheless, it is still challenging for FL to deal with user heterogeneity in their local data distribution in the real-world FL scenario, and this issue becomes even more severe in multi-label image classification.
Adaptive Hinge Balance Loss for Document-Level Relation Extraction
Jize-W/HingeABL
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EMNLP 2023
In this paper, we propose to downweight the easy negatives by utilizing a distance between the classification threshold and the predicted score of each relation.
Long-tailed multi-label classification with noisy label of thoracic diseases from chest X-ray
This work establishes a foundation for robust CAD methods, achieving a balance in identifying a spectrum of thoracic diseases in CXRs.
Scalable Label Distribution Learning for Multi-Label Classification
ailearn-ml/sldl
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Most existing MLC methods are based on the assumption that the correlation of two labels in each label pair is symmetric, which is violated in many real-world scenarios.
Category-Wise Fine-Tuning for Image Multi-label Classification with Partial Labels
maxium0526/category-wise-fine-tuning
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International Conference on Neural Information Processing 2023
A single model submitted to the competition server for the official evaluation achieves mAUC 91. 82% on the test set, which is the highest single model score in the leaderboard and literature.
