Multi-Label Image Classification
39 papers with code • 4 benchmarks • 8 datasets
The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.
Benchmarks
These leaderboards are used to track progress in Multi-Label Image Classification
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COCO
BigEarthNet
Sewer-ML
dacl10k
Bengali.AI Handwritten Graphemes
Most implemented papers
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
arnoweng/CheXNet
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CVPR 2017
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.
Residual Attention: A Simple but Effective Method for Multi-Label Recognition
Kevinz-code/CSRA
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ICCV 2021
Multi-label image recognition is a challenging computer vision task of practical use.
Improving Pairwise Ranking for Multi-label Image Classification
raingo-ur/mll-tf
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CVPR 2017
Pairwise ranking, in particular, has been successful in multi-label image classification, achieving state-of-the-art results on various benchmarks.
SSL4EO-S12: A Large-Scale Multi-Modal, Multi-Temporal Dataset for Self-Supervised Learning in Earth Observation
Self-supervised pre-training bears potential to generate expressive representations without human annotation.
Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification
Analysis of the learned SRN model demonstrates that it can effectively capture both semantic and spatial relations of labels for improving classification performance.
General Multi-label Image Classification with Transformers
Multi-label image classification is the task of predicting a set of labels corresponding to objects, attributes or other entities present in an image.
Multi-Label Learning from Single Positive Labels
elijahcole/single-positive-multi-label
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CVPR 2021
When the number of potential labels is large, human annotators find it difficult to mention all applicable labels for each training image.
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels
xinyu1205/robust-loss-mlml
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Multi-label learning in the presence of missing labels (MLML) is a challenging problem.
Self-supervised Learning in Remote Sensing: A Review
In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.
CNN-RNN: A Unified Framework for Multi-label Image Classification
Lin-Zhipeng/CNN-RNN-A-Unified-Framework-for-Multi-label-Image-Classification
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CVPR 2016
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image.
