Multi-Label Image Classification

51 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.


Use these libraries to find Multi-Label Image Classification models and implementations

Most implemented papers

Deep Residual Learning for Image Recognition

tensorflow/models 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 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 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 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

zhu-xlab/ssl4eo-s12 13 Nov 2022

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

zhufengx/SRN_multilabel CVPR 2017

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

QData/C-Tran CVPR 2021

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 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 13 Dec 2021

Multi-label learning in the presence of missing labels (MLML) is a challenging problem.

Self-supervised Learning in Remote Sensing: A Review

zhu-xlab/ssl4eo-review 27 Jun 2022

In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.