Multi-Label Image Classification
59 papers with code • 7 benchmarks • 10 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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Libraries
Use these libraries to find Multi-Label Image Classification models and implementationsDatasets
MS COCO
MSCOCO
VizWiz-Classification
Sewer-ML
Bengali.AI Handwritten Graphemes
LADI v2
dacl10k
Most implemented papers
Deep Residual Learning for Image Recognition
KaimingHe/resnet-1k-layers
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CVPR 2016
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
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.
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
kai-tub/rico-hdl
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To construct reBEN, we initially consider the Sentinel-1 and Sentinel-2 tiles used to construct the BigEarthNet dataset and then divide them into patches of size 1200 m x 1200 m. We apply atmospheric correction to the Sentinel-2 patches using the latest version of the sen2cor tool, resulting in higher-quality patches compared to those present in BigEarthNet.
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.
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.
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.
