Multi-Label Image Classification
29 papers with code • 2 benchmarks • 3 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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BigEarthNet
Sewer-ML
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.
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.
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.
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.
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.
Structured Label Inference for Visual Understanding
daveboat/structured_label_inference
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In this paper, we exploit this rich structure for performing graph-based inference in label space for a number of tasks: multi-label image and video classification and action detection in untrimmed videos.
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
A Baseline for Multi-Label Image Classification Using An Ensemble of Deep Convolutional Neural Networks
hellowangqian/multi-label-image-classification
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Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.
